Computer-based systems configured for managing mesh networks having integrated roofing components and methods of use thereof

ABSTRACT

Systems and methods of the present disclosure enable mesh network capacity management via network metering using a processor an integrated roofing mesh network node in a mesh network to receive and transmit data packets in the mesh network. Each data packet includes a source address, a destination address, and a payload of data. The processor determines passthrough traffic including a subset of data packets routed between radio nodes of the mesh network through the gateway based on the source address and the destination address of each data packet and an address associated with the gateway. The processor determines a passthrough data capacity based on the payload of each data packet in the subset and determines a metric based on the passthrough data capacity to signify an amount of mesh network bandwidth provided by the integrated roofing mesh network node.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 17/868,544, filed Jul. 19, 2022, which claims priority to U.S.Provisional Application No. 63/229,815, filed on Aug. 5, 2021, which areincorporated herein by reference in their entirety.

FIELD OF TECHNOLOGY

The present disclosure generally relates to computer-based systemsconfigured to manage mesh networks by performing various activities suchas, without limitations, tracking bandwidth contribution to the meshnetwork by mesh network nodes that may have component(s) integrated intovarious roofing materials of various structures.

BACKGROUND OF TECHNOLOGY

For example, mesh networks rely on each mesh node for routing trafficfrom one or more sources to one or more destinations. For example,tracking contributions of each node to a communication of data from asource to a destination is practical application that allows, forexample, without limitations, to optimize a mesh network and incentivizevarious participants to contribute their computing devices to beprogrammed as mesh nodes.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides a technicallyimproved computer-based method that includes at least the followingsteps of receiving, by a processor of a gateway of an integrated roofingmesh network node in a mesh network of other nodes, a plurality ofreceived data packets from the mesh network; transmitting, by theprocessor, a plurality of transmitted data packets to the mesh network;wherein each data packet of the plurality of received data packets andthe plurality of transmitted data packets comprises: i) a source addressof a sending node, ii) a destination address of a receiving node, andiii) a payload of data; comparing, by the processor, the source addressand the destination address of each data packet with an addressassociated with the gateway; determining, by the processor, passthroughtraffic based at least in part on: i) the address associated with thegateway, and ii) the source address and the destination address of eachdata packet; wherein the passthrough traffic comprises a subset of theplurality of received data packets and the plurality of the transmitteddata packets that is routed between two or more radio nodes of the meshnetwork through the gateway of the integrated roofing mesh network nodebased at least in part on the source address and the destination addressof each data packet; determining, by the processor, a passthrough datacapacity based at least in part on the payload of data of each datapacket in the subset; determining, by the processor, a metric based atleast in part on the passthrough data capacity; and communicating, bythe processor, the metric to service provider to notify the serviceprovider of an amount of mesh network bandwidth provided by thepassthrough data capacity of the integrated roofing mesh network node.

In some embodiments, the present disclosure provides a technicallyimproved computer-based system that includes at least the followingcomponents of a gateway of an integrated roofing mesh network node incommunication with a mesh network of other nodes, wherein the gatewaycomprises a processor configured to execute software instructions. Thesoftware instructions, when executed, cause the processor to performsteps to: receive a plurality of received data packets from the meshnetwork; transmit a plurality of transmitted data packets to the meshnetwork; wherein each data packet of the plurality of received datapackets and the plurality of transmitted data packets comprises: i) asource address of a sending node, ii) a destination address of areceiving node, and iii) a payload of data; compare the source addressand the destination address of each data packet with an addressassociated with the gateway; determine passthrough traffic based atleast in part on: i) the address associated with the gateway, and ii)the source address and the destination address of each data packet;wherein the passthrough traffic comprises a subset of the plurality ofreceived data packets and the plurality of the transmitted data packetsthat is routed between two or more radio nodes of the mesh networkthrough the gateway of the integrated roofing mesh network node based atleast in part on the source address and the destination address of eachdata packet; determine a passthrough data capacity based at least inpart on the payload of data of each data packet in the subset; determinea metric based at least in part on the passthrough data capacity; andcommunicate the metric to service provider to notify the serviceprovider of an amount of mesh network bandwidth provided by thepassthrough data capacity of the integrated roofing mesh network node.

In some embodiments, the present disclosure provides another technicallyimproved computer-based method that includes at least the followingsteps of receiving, by a processor of a gateway of an integrated roofingmesh network node in a mesh network of other nodes, a data packetassociated with the mesh network; wherein the data packet comprises: i)a header specifying: a virtual mesh network identifier identifying avirtual mesh network operating as a tenant of the mesh network, a sourceaddress of a sending node, and a destination address of a receivingnode, and iii) a payload of data; identifying, by the processor, thedata packet as passthrough traffic based at least in part on: i) theaddress associated with the gateway, and ii) the address and thedestination address of the data packet; wherein the passthrough trafficcomprises data traffic that is routed between two or more radio nodes ofthe mesh network through the gateway of the integrated roofing meshnetwork node based at least in part on the source address and thedestination address of the data packet; determining, by the processor, apassthrough data capacity based at least in part on the payload of dataof the data packet; determining, by the processor, a service provider ofthe mesh network based at least in part on the virtual mesh networkidentifier; determining, by the processor, a service provider-specificmetric based at least in part on the passthrough data capacity and theservice provider of the mesh network; and communicating, by theprocessor, the metric to the service provider to notify the serviceprovider of an amount of mesh network bandwidth provided by thepassthrough data capacity of the integrated roofing mesh network node.

In some embodiments, systems and/or methods of the present disclosurefurther include determining, by the processor, consumed traffic based atleast in part on: i) the address associated with the processor, and ii)the source address and the destination address of each data packet;wherein the consumed traffic comprises a second subset of the pluralityof received data packets and the plurality of the transmitted datapackets that is routed between the integrated roofing mesh network nodeand radio node of the mesh network based at least in part on the sourceaddress and the destination address of each data packet; determining, bythe processor, a consumed data capacity based at least in part on thepayload of data of each data packet in the second subset; anddetermining, by the processor, the metric based at least in part on thepassthrough data capacity and the consumed data capacity.

In some embodiments, systems and/or methods of the present disclosurefurther include wherein the metric comprises a ratio of the passthroughdata capacity to the consumed data capacity.

In some embodiments, systems and/or methods of the present disclosurefurther include determining, by the processor, a size of the payload ofdata of each data packet in the subset.

In some embodiments, systems and/or methods of the present disclosurefurther include wherein the passthrough data capacity comprises a sum ofthe size of the payload of data of each data packet in the subset over afirst period of time.

In some embodiments, systems and/or methods of the present disclosurefurther include determining, by the processor, a data communicationprioritization parameter based at least in part on the passthrough datacapacity; wherein the data communication prioritization parametercomprises relative priority of communication of the passthrough datatraffic and non-passthrough data traffic; and instructing, by theprocessor, the gateway to prioritize communication of a plurality offuture received data packets and a plurality of future transmitted datapackets based at least in part on the data communication prioritizationparameter.

In some embodiments, systems and/or methods of the present disclosurefurther include determining, by the processor, a tenant mesh networkassociated with each data packet in the subset; wherein the mesh networkof radio nodes comprises a physical infrastructure layer; wherein aservice layer utilizes the physical infrastructure layer for dataservice, the service layer comprising a plurality of tenant meshnetworks sharing the mesh network of the physical infrastructure layer;determining, by the processor, the passthrough data capacity associatedwith the tenant mesh network based at least in part on the payload ofdata of each data packet associated with the tenant mesh network in thesubset; determining, by the processor, tenant-specific metric based atleast in part on the passthrough data capacity; and communicating, bythe processor, the tenant-specific metric to a service providerassociated with the tenant mesh network.

In some embodiments, systems and/or methods of the present disclosurefurther include detecting, by the processor, a signal strength of theintegrated roofing mesh network node with each radio node of the meshnetwork; and utilizing, by the processor, a data communicationprediction machine learning model to estimate a consumed data capacityfor a next period of time; wherein the consumed data capacity comprisesa second subset of the plurality of received data packets and theplurality of the transmitted data packets that is routed between theintegrated roofing mesh network node and radio node of the mesh networkbased at least in part on the source address and the destination addressof each data packet.

In some embodiments, systems and/or methods of the present disclosurefurther include wherein the mesh network comprises a fifth generationcellular (5G) network, the integrated roofing mesh network nodecomprises an integrated 5G radio.

In some embodiments, systems and/or methods of the present disclosurefurther include wherein the mesh network comprises a physicalinfrastructure layer comprising of the integrated roofing mesh networknode and the other nodes, and wherein the mesh network comprises amulti-tenancy virtual network layer having a plurality of virtual meshnetworks.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, wherein like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIG. 1 is a block diagram illustrating a roofing integrated mesh networkgateway in accordance with one or more embodiments of the presentdisclosure.

FIG. 2 is a block diagram illustrating a structure having a roofingintegrated mesh network gateway in accordance with one or moreembodiments of the present disclosure.

FIG. 3 is a block diagram illustrating a mesh network made of integratedroofing mesh network gateways in accordance with one or more embodimentsof the present disclosure.

FIG. 4 illustrates a flowchart of an illustrative mesh networkmanagement methodology that employs an integrated roofing mesh networkgateway in accordance with one or more embodiments of the presentdisclosure.

FIG. 5 illustrates a flowchart of an illustrative mesh network packetrouting management methodology that employs an integrated roofing meshnetwork gateway in accordance with one or more embodiments of thepresent disclosure.

FIG. 6 illustrates a flowchart of an illustrative mesh network packetpayload management methodology that employs an integrated roofing meshnetwork gateway in accordance with one or more embodiments of thepresent disclosure.

FIG. 7 illustrates a flowchart of an illustrative mesh network packettraffic management methodology that employs an integrated roofing meshnetwork gateway in accordance with one or more embodiments of thepresent disclosure.

FIG. 8 illustrates a flowchart of an illustrative mesh networkmanagement prediction machine learning model that is trained based atleast in part on data associated with an integrated roofing mesh networkgateway in accordance with one or more embodiments of the presentdisclosure.

FIG. 9 depicts a block diagram of an mesh network managementcomputer-based system/platform that employs an integrated roofing meshnetwork gateway in accordance with one or more embodiments of thepresent disclosure.

FIG. 10 depicts a block diagram of another mesh network managementcomputer-based system/platform that employs an integrated roofing meshnetwork gateway in accordance with one or more embodiments of thepresent disclosure.

FIG. 11 depicts an illustrative diagram of an implementation of a cloudcomputing architecture of an computer-based system that employs anintegrated roofing mesh network gateway in accordance with someembodiments of the present disclosure.

FIG. 12 depicts an illustrative diagram of another implementation of acloud computing architecture of a mesh network management computer-basedsystem that employs an integrated roofing mesh network gateway inaccordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

In addition, the term “based on” is not exclusive and allows for beingbased on additional factors not described, unless the context clearlydictates otherwise. In addition, throughout the specification, themeaning of “a,” “an,” and “the” include plural references. The meaningof “in” includes “in” and “on.”

As used herein, the terms “and” and “or” may be used interchangeably torefer to a set of items in both the conjunctive and disjunctive in orderto encompass the full description of combinations and alternatives ofthe items. By way of example, a set of items may be listed with thedisjunctive “or”, or with the conjunction “and.” In either case, the setis to be interpreted as meaning each of the items singularly asalternatives, as well as any combination of the listed items.

FIGS. 1 through 12 illustrate systems and methods for managing meshnetworks having mesh nodes of integrated roofing mesh network gateways,by performing various activities such as, without limitations, bandwidthtracking (e.g., bandwidth consumption, bandwidth use for passthroughtraffic, etc.) to measure and/or track participation in a meshednetwork. The following embodiments provide technical solutions andtechnical improvements that overcome technical problems, drawbacksand/or deficiencies in the technical fields involving mesh networkmanagement, including improvements in measuring and/or trackingcontributions of node(s), having one or more integrated roofing meshnetwork gateways, to a mesh network. As explained in more detail, below,technical solutions and technical improvements herein may include one ormore aspects of an improved passthrough data capacity measurement andtracking, a mesh network optimization, and incentivization of networkparticipation. Moreover, various practical applications disclosed hereinprovide further practical benefits to users and operators that are alsonew and useful improvements in the art.

In some embodiments, a mesh network node may be integrated into roofingmaterial, such as, without limitations, a shingle, underlayment, ridgevent, chimney, roof vent, or other roofing structure. In someembodiment, the mesh network node may relay data between other nodes inthe mesh network as a part of network traffic routing. In someembodiments, some or all of the network traffic passing through the meshnetwork node may be relayed to other nodes on the mesh network, or maybe provided to one or more computing device(s) in a structure associatedwith the mesh network node, or any combination thereof. For example, themesh network node may provide connectivity between computing device(s)inside of a structure whose roof has one or more integrated roofing meshnetwork gateways associated with one or more service provider and/oradditional mesh network nodes. In another example, the mesh network nodemay provide connectivity between computing device(s) and/or mesh networknodes outside of the structure to expand service provider reach.Accordingly, a mesh network node integrated into roofing material(“integrated roofing mesh network node”) may act as a node in theservice provider's network to expand the reach of the network.

In some embodiments, the integrated roofing mesh network node mayinclude components and functionality (e.g., via a gateway and othercomputing components) to measure the bandwidth usage of the occupant, aswell as the bandwidth usage of all non-occupant traffic passing throughthe integrated roofing mesh network node. The measurement of bandwidthusage may be used to analyze network performance, offer incentives basedon how much additional traffic the occupant's node enabled the carrierto handle, among other network performance and participation uses. Insome embodiments, the occupant may receive an incentive just fromallowing their roof to be used as part of the mesh network and theincentive amount may scale based on the amount of traffic their roofenables. In some embodiments, the occupant may be a customer of aservice provider that communicates network traffic through theintegrated roofing mesh network node. Thus, the incentive may include,e.g., a rebate or credit on a data consumption bill or other data useincentive. For example, at the end of a billing period, a total amountof bandwidth consumed by the occupant will be compared against thebandwidth of other traffic passing through their node, and occupant canreceive credit based on the amount of traffic flowing through theintegrated roofing mesh network node. In another example, where theoccupant is not a consumer of data communicated through the integratedroofing mesh network node, the occupant may receive an incentive justfrom allowing their roof to be used as part of the mesh network via,e.g., cash, partner rewards, gift cards, roofing and/or structuremaintenance benefits, among other incentives or any combination thereof.

Accordingly, in some embodiments, the integrated roofing mesh networknode may monitor the bandwidth usage passing through the integratedroofing mesh network node, and measure the amount of bandwidth consumedby the occupant (“consumed data capacity”) versus the amount ofbandwidth provided to the network via passing external traffic betweenother nodes (“passthrough data capacity”). In some embodiments, theintegrated roofing mesh network node or other computing device and/orsystem may use the consumed data capacity and passthrough data capacityto calculate an incentive or compensation for the occupant forparticipating in the network.

In some embodiments, such an incentive allows network users to monetizeor take advantage of their integrated roofing mesh network nodes andprovides incentive for the users to help service provides expand networkreach. As a result, the incentive may contribute to the creation of anetwork effect where the users are incentivized expand the network ofdevices that can communicate through their node, increasing theperformance of the network and allowing for additional users to utilizethe network.

While blockchain-related decentralized wireless infrastructure relyingon proof-of-work and/or proof-of-stake may be employed to generatecryptocurrency based mechanisms for tracking participation andcontribution, such a technique relies on complicated technology andsufficient decentralized participation. Measuring consumed data capacityand passthrough data capacity, on the other hand, enables more efficienttracking and measurement of participation and contribution such thatcheaper equipment may be used, bandwidth can be tracked and monitoredusing fewer resources, and more users may participate with their ownnodes.

Some embodiments of the present disclosure relate to methods and systemsthat include the integrated roofing mesh network node. As defined hereinan “integrated roofing mesh network node” is a roofing accessory with atleast one 5G-infrastructure-supporting (“5G-enabled”) electroniccomponent. In some embodiments, the at least one5G-infrastructure-supporting electronic component is embedded within atleast one roofing accessory component. In another embodiments, the atleast one 5G-infrastructure-supporting electronic component is directlyor indirectly attached to at least one roofing accessory component.

Some embodiments of the present disclosure relate to integrated roofingmesh network node. Some embodiments of the present disclosure include aplurality of integrated roofing mesh network nodes. Some embodiments ofthe present disclosure include at least three integrated roofing meshnetwork nodes. Some embodiments of the present disclosure include atleast five integrated roofing mesh network nodes. Some embodiments ofthe present disclosure include at least ten integrated roofing meshnetwork nodes. Some embodiments of the present disclosure include atleast fifty integrated roofing mesh network nodes. Some embodiments ofthe present disclosure include at least one hundred integrated roofingmesh network nodes. Some embodiments of the present disclosure includeat least one-thousand integrated roofing mesh network nodes.

In some embodiments, there are 1 to 10,000 integrated roofing meshnetwork nodes. In some embodiments there are 1 to 5000 integratedroofing mesh network nodes. In some embodiments, there are 1 to 1000integrated roofing mesh network nodes. In some embodiments, there are 1to 100 integrated roofing mesh network nodes. In some embodiments, thereare 1 to 50 integrated roofing mesh network nodes. In some embodiments,there are 1 to 25 integrated roofing mesh network nodes. In someembodiments, there are 1 to 10 integrated roofing mesh network nodes. Insome embodiments, there are 1 to 5 integrated roofing mesh networknodes. In some embodiments, there are 1 to 2 integrated roofing meshnetwork nodes.

In some embodiments, there are 2 to 10,000 integrated roofing meshnetwork nodes. In some embodiments, there are 5 to 10,000 integratedroofing mesh network nodes. In some embodiments, there are 10 to 10,000integrated roofing mesh network nodes. In some embodiments, there are 50to 10,000 integrated roofing mesh network nodes. In some embodiments,there are 100 to 10,000 integrated roofing mesh network nodes. In someembodiments, there are 500 to 10,000 integrated roofing mesh networknodes. In some embodiments, there are 1000 to 10,000 integrated roofingmesh network nodes. In some embodiments, there are 5000 to 10,000integrated roofing mesh network nodes.

In some embodiments, there are 2 to 5000 integrated roofing mesh networknodes. In some embodiments, there are 5 to 1000 integrated roofing meshnetwork nodes. In some embodiments, there are 10 to 5000 integratedroofing mesh network nodes. In some embodiments, there are 50 to 100integrated roofing mesh network nodes. In some embodiments, there are 60to 90 integrated roofing mesh network nodes. In some embodiments, thereare 70 to 80 integrated roofing mesh network nodes.

Non-limiting examples of the at least one roofing accessory component ofthe integrated roofing mesh network node include: roofing caps, laminateroofing accessories, roofing sheets, ridge caps, ridge vents, roofingframes, roofing shingles and the like, or any combination thereof.Additional non-limiting examples of the at least one portion of theroofing accessory are found in U.S. Pat. Nos. 7,165,363 and 10,180,001,both of which are incorporated by reference in their respectiveentireties.

FIG. 1 is a block diagram illustrating an integrated roofing meshnetwork gateway in accordance with one or more embodiments of thepresent disclosure.

In some embodiments, an integrated roofing mesh network gateway 100 maycommunicated with a user device 160 to provide a network connection tothe user device 160. In some embodiments, the integrated roofing meshnetwork gateway 100 may include a mesh network radio 105 that isconfigured to communicate with a mesh network 180 in order to providethe network connection. Accordingly, in some embodiments, the meshnetwork radio 105 may include a suitable radio, such as, e.g., areceiver and transmitter circuits, software-defined receiver andsoftware-defined transmitter software and hardware elements, among otherradio hardware and/or software. In some embodiments, the mesh networkradio 105 may include, e.g., one or more antennas and/or one or morearrays of antennas.

In some embodiments, the mesh network radio 105 may emit 5G signalsusing one or more antennae integrated into a roof of a structure, e.g.,via a roofing material and/or roofing accessory and/or roofing accessorycomponent. For example, a dielectric antenna may be embedded in apolymer sized to cover one or more frame components such as, withoutlimitation, an electronics compartment housing radio hardware and/orsoftware components as described above. In some embodiments, thedielectric antenna may be a patch antenna, or other suitable antenna forembedding in the cover such that the cover may form an antenna modulecovering the electronic components. As a result, the cover may serve asboth a roofing accessory to weatherproof a roof of a structure, as wellas an antenna for a mesh network.

In some embodiments, the integrated roofing mesh network node 170includes at least one embedded antenna. As used herein, the term“antenna” or “antennae” can refer to a device that is part of atransmitting or receiving system to transmit or receive wirelesssignals. In some embodiments, the at least one embedded antenna isconfigured to perform at least one of the following operations:receiving electromagnetic waves (e.g., 5G signals), transmittingelectromagnetic waves (e.g., 5G signals), or any combination thereof.

In some embodiments, the integrated roofing mesh network node 170 isconfigured to support at least one signal propagation strategy. The atleast one signal propagation strategy includes, but is not limited to,at least one of: many inputs—many outputs (MIMO), beam forming mesh, thelike, or any combination thereof.

In some embodiments, the at least one embedded antenna is at least onedielectric antenna. In some embodiments, the at least one dielectricantenna takes the form of at least one dielectric antenna array. In someembodiments, the at least one dielectric antenna array includes aplurality of dielectric antennas configured to wirelessly receive acontrollable beam in response to electromagnetic waves. In someembodiments, the at least one dielectric antenna array includes aplurality of dielectric antennas configured to wirelessly transmit acontrollable beam in response to the electromagnetic waves. In someembodiments, the at least one dielectric antenna array includes aplurality of dielectric antennas configured to wirelessly transmit andreceive a controllable beam in response to the electromagnetic waves.

In some embodiments, the dielectric antenna is embedded within the coveror is covered by the cover within the electronics compartment describedabove. Accordingly, the cover may be constructed from a material thathas a minimal effect on the 5G signals emitted by the dielectricantenna, such as a material that is transparent to mmWave signals, thuscausing sufficiently low attenuation to the mmWave signals for a stabledata transmission or reception. For example, the cover may include apolymer, including engineered polymers, such as the D30™ Gear4™ and 5GSignal Plus material having microvoids for reducing mmWave attenuation,as disclosed by “D3O INTRODUCES 5G SIGNAL PLUS TECHNOLOGY”, D30 PressRelease,https://www.d3o.com/partner-support/press-releases/d3o-introduces-5g-signal-plus/>(accessed, 1 Sep. 2020), herein incorporated by reference in itsentirety.

In some embodiments, the mesh network may include any suitable meshnetwork, such as, e.g., a mesh cellular network, a mesh WiFi network, amesh Bluetooth network, or any suitable wireless networking technologynetworked according to mesh networking techniques. In some embodiments,mesh networking may include, e.g., a network topology in which theinfrastructure nodes (e.g., bridges, switches, and other infrastructuredevices) connect directly, dynamically and non-hierarchically to as manyother nodes as possible and cooperate with one another to efficientlyroute data from/to clients. The lack of dependency on one node allowsfor every node to participate in the relay of information. In someembodiments, mesh networks dynamically self-organize and self-configure,which can reduce installation overhead. The ability to self-configureenables dynamic distribution of workloads, particularly in the event afew nodes should fail. This in turn contributes to fault-tolerance andreduced maintenance costs.

In some embodiments, the mesh network 180 may include multiple serviceproviders operating in a multi-tenancy arrangement on a common physicalinfrastructure. In some embodiments, the integrated roofing mesh networknodes 170 across the mesh network 180 may define the total physicalinfrastructure of the mesh network, and each service provider may havevirtual networks connected to respective backhaul networks for broadernetwork coverage.

In some embodiments, the integrated roofing mesh network gateway 100 mayutilize the mesh network radio 105 to participate in the mesh network180 for the routing of network traffic amongst nodes and clients on themesh network 180. In some embodiments, the term node is employed torefer to any communication endpoint connected to the mesh network 180,including, e.g., the integrated roofing mesh network gateway 100 of anintegrated roofing mesh network node 170, a user computing device incommunication with the mesh network 180 (e.g., a desktop computingdevice, a mobile computing device, a person digital assistant (PDA), awearable device such as a smartwatch or smart-glasses, anInternet-of-Things device, etc.) or any other suitable devicecommunicating on the mesh network 180 or any combination thereof.

Non-limiting examples of the user computing device may include at leastone personal computer (PC), laptop computer, ultra-laptop computer,tablet, touch pad, portable computer, handheld computer, palmtopcomputer, personal digital assistant (PDA), cellular telephone,combination cellular telephone/PDA, television, smart device (e.g.,smart phone, smart tablet or smart television), mobile internet device(MID), messaging device, data communication device, and the like.

In some embodiments, the integrated roofing mesh network gateway 100 mayemploy one or more processor(s) 109 to control the mesh network radio105 for mesh network 180 communication, including the routing of dataover the mesh network 180 and/or to/from a user device 160. In someembodiments, the processor(s) 109 may include at least one processor,microprocessor, circuit, circuit element (e.g., transistors, resistors,capacitors, inductors, and so forth), integrated circuit, applicationspecific integrated circuit (ASIC), programmable logic device (PLD),digital signal processor (DSP), field programmable gate array (FPGA),logic gate, register, semiconductor device, chip, microchip, chip set,and so forth. In some embodiments, the processor(s) 109 may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

In some embodiments, the processor(s) 109 may execute instructionsstored in at least one storage component. Non-limiting examples of theat least one storage component may include: read only memory (ROM) 111,random access memory (RAM) 103, and/or a storage device 101 using, e.g.,magnetic disk storage media; optical storage media; flash memorydevices; electrical, optical, acoustical or other forms of propagatedsignals (e.g., carrier waves, infrared signals, digital signals, etc.),or any combination thereof.

In some embodiments, the integrated roofing mesh network gateway 100 mayutilize the processor(s) 109 to monitor data traffic through theintegrated roofing mesh network gateway 100 and track, measure, manageand predict data capacity usage. Accordingly, in some embodiments, theprocessor(s) 109 may monitor protocol data units to determine whethereach unit of data is associated with the user device 160 or integratedroofing mesh network gateway 100, or whether each unit of data isassociated with network traffic routed from an external source to anexternal destination.

In some embodiments, a protocol data unit (PDU) is a single unit ofinformation transmitted among peer entities of a computer network. A PDUmay include protocol-specific control information and user data. In thelayered architectures of communication protocol stacks, each layerimplements protocols tailored to the specific type or mode of dataexchange. For example, the Transmission Control Protocol (TCP)implements a connection-oriented transfer mode, and the PDU of thisprotocol is called a segment, while the User Datagram Protocol (UDP)uses datagrams as protocol data units for connectionless communication.A layer lower in the Internet protocol suite, at the Internet layer, thePDU is called a packet, irrespective of its payload type.

In some embodiments, to illustrate the monitoring and metering ofbandwidth according to aspects of the present disclosure, the PDU isdescribed as a packet in a network such as the Internet. However, theprinciples described herein are applicable to any suitable networkingprotocol using any suitable PDU. In some embodiments a packet mayinclude a header and a payload. The header may include of fixed andoptional fields. The payload appears immediately after the header.

In some embodiments, a packet header may include addresses, length,priority, among other fixed and/or option fields. In some embodiments,an address may include routing of network packets requires two networkaddresses, the source address of the sending host, and the destinationaddress of the receiving host. In some embodiments, there may be a fieldto identify the overall packet length. However, in some types ofnetworks, the length is implied by the duration of the transmission. Insome embodiments, the packet may include a priority field. Some networksimplement quality of service which can prioritize some types of packetsabove others. This field indicates which packet queue should be used; ahigh priority queue is emptied more quickly than lower priority queuesat points in the network where congestion is occurring.

In some embodiments, the payload may include the data that is carried onbehalf of an application. The data of packet may be of variable length,up to a maximum that is set by the network protocol and sometimes theequipment on the route. When necessary, some networks can break a largerpacket into smaller packets.

In some embodiments, the processor(s) 109 may implement a packet routingengine 110 to control local routing of data packets communicated toand/or from the mesh network radio 105. Some data packets may beassociated with the user device 160 and/or the integrated roofing meshnetwork gateway 100, while some data packets may be associated withexternal nodes on the mesh network 180. Accordingly, the packet routingengine 110 may examine the addresses of each packet to identify whethereach packet is associated with a known source and/or destination (e.g.,the user device 160, the integrated roofing mesh network gateway 100,etc.) or whether the packet is associated only with unknown sources anddestinations (e.g., other nodes on the mesh network 180). Based on theaddress, the packet routing engine 110 may route each packet either tothe mesh network 180 via mesh network radio 105 or to the user device160, e.g., via an output interface 107. Similarly, packets created bythe user device 160 may route to the mesh network 180 according to theaddresses via the input interface 113 and the mesh network radio 105.

Moreover, in some embodiments, where the integrated roofing mesh networkgateway 100 is part of a physical infrastructure layer with virtual meshnetworks operating thereon according to a multi-tenancy arrangement, thepacket routing engine 110 may log, e.g., in the storage device 101and/or the RAM 103, the addresses as well as the service provider withwhich each packet is associated. Thus, each packet may be attributed tointernal traffic or external traffic, as well as a particular serviceprovider.

In some embodiments, each packet routed can be tracked by a packettracking engine 120. Thus, packets associated with the user device 160,e.g., as the source or the destination of the packets, may be tracked asconsumed data communicated across the mesh network 180. The consumeddata refers to inbound data and/or outbound data attributable to theuser device 160. Similarly, packets associated only with external nodes(e.g., nodes on the mesh network 180 that are routed through theintegrated roofing mesh network gateway 100) and not the user device 160may be tracked as passthrough data that passes through the integratedroofing mesh network gateway 100 to provide a communication path in themesh network 180. In some embodiments, the packet tracking engine 120may determine the size of the payload of each packet to assess theamount of data used by the consumed data and by the passthrough data.The size of each packet may then be added to the log to log the size ofeach packet attributed to internal traffic or external traffic, as wellas a particular service provider.

In some embodiments, a data tracking engine 130 may utilize the size ofthe payload of each packet of consumed data and each packet ofpassthrough data to measure the bandwidth usage attributable to theconsumed data and the passthrough data. The data tracking engine 130 mayaccess the log of packets and packet sizes and produce a datacommunication metric for each of the consumed data and the passthroughdata to measure the consumed data capacity and the passthrough datacapacity. In some embodiments, the data tracking engine 130 mayformulate the data communication metric for each service provider, foreach of the consumed data capacity and the passthrough data capacityand/or for the physical infrastructure layer of the mesh network 180.

In some embodiments, the term bandwidth may refer to the net bit rate‘peak bit rate’, ‘information rate,’ or physical layer ‘useful bitrate’, channel capacity, or the maximum throughput of a logical orphysical communication path, which, in the case, may include the meshnetwork 180, the integrated roofing mesh network gateway 100 or both. Insome embodiments, the maximum rate that can be sustained on a link arelimited by the Shannon—Hartley channel capacity for communicationsystems, which is dependent on the bandwidth in hertz and the noise onthe channel.

In some embodiments, the consumed data capacity in bit per second (bps),corresponds to achieved throughput or goodput, e.g., the average rate ofsuccessful data transfer through a communication path. The consumed datacapacity can be affected by technologies such as bandwidth shaping,bandwidth management, bandwidth throttling, bandwidth cap, bandwidthallocation (for example bandwidth allocation protocol and dynamicbandwidth allocation), etc. A bit stream's bandwidth is proportional tothe average consumed signal bandwidth in hertz (the average spectralbandwidth of the analog signal representing the bit stream) during aparticular time period.

In some embodiments, the term channel bandwidth may be confused withuseful data throughput (or goodput). For example, a channel with ‘x’ bpsmay not necessarily transmit data at x rate, since protocols,encryption, and other factors can add appreciable overhead. Forinstance, much internet traffic uses the transmission control protocol(TCP), which requires a three-way handshake for each transaction.Although in many modern implementations the protocol is efficient, itdoes add significant overhead compared to simpler protocols. Also, datapackets may be lost, which further reduces the useful data throughput.In general, for any effective digital communication, a framing protocolis needed; overhead and effective throughput depends on implementation.Useful throughput is less than or equal to the actual channel capacityminus implementation overhead.

In some embodiments, the data tracking engine 130 may measure the datacommunication metric over a period of time, such as, e.g., a billingperiod defined by a service provider, such as a service provider thatprovides data coverage via the mesh network 180. Accordingly, the datacommunication metric may define, e.g., a sum, average, median, or otherstatistical aggregation of bandwidth per time per service provider. Insome embodiments, the bandwidth may be measured as goodput (e.g., bitscommunicated through the integrated roofing mesh network gateway 100 pertime). In some embodiments, the time period may include, e.g., a second,a minute, an hour, a day, a week, a month, or other suitable period offor defining consumed data capacity and passthrough data capacity usage.In some embodiments, the data communication metric may be one or moretokens on a blockchain, where the one or more tokens accrue invalue/quantity/amount as the passthrough data is communicated by theintegrated roofing mesh network gateway 100.

Accordingly, in some embodiments, the mesh network and the integratedroofing mesh network gateway 100 may be configured interact and/or tostore data in one or more private and/or private-permissionedcryptographically-protected, distributed database such as, withoutlimitation, a blockchain (distributed ledger technology), Ethereum(Ethereum Foundation, Zug, Switzerland), and/or other similardistributed data management technologies. For example, as utilizedherein, the distributed database(s), such as distributed ledgers ensurethe integrity of data by generating a chain of data blocks linkedtogether by cryptographic hashes of the data records in the data blocks.For example, a cryptographic hash of at least a portion of data recordswithin a first block, and, in some cases, combined with a portion ofdata records in previous blocks is used to generate the block addressfor a new digital identity block succeeding the first block. As anupdate to the data records stored in the one or more data blocks, a newdata block is generated containing respective updated data records andlinked to a preceding block with an address based upon a cryptographichash of at least a portion of the data records in the preceding block.In other words, the linked blocks form a blockchain that inherentlyincludes a traceable sequence of addresses that can be used to track theupdates to the data records contained therein. The linked blocks (orblockchain) may be distributed among multiple network nodes within acomputer network such that each node may maintain a copy of theblockchain. Malicious network nodes attempting to compromise theintegrity of the database must recreate and redistribute the blockchainfaster than the honest network nodes, which, in most cases, iscomputationally infeasible. In other words, data integrity is guaranteedby the virtue of multiple network nodes in a network having a copy ofthe same blockchain. In some embodiments, as utilized herein, a centraltrust authority for sensor data management may not be needed to vouchfor the integrity of the distributed database hosted by multiple nodesin the network.

In some embodiments, the exemplary distributed blockchain-type ledgerimplementations of the present disclosure with associated devices may beconfigured to affect transactions involving Bitcoins and othercryptocurrencies into one another and also into (or between) so-calledFIAT money or FIAT currency and vice versa.

In some embodiments, the exemplary distributed blockchain-type ledgerimplementations of the present disclosure with associated devices areconfigured to utilize smart contracts that are computer processes thatfacilitate, verify and/or enforce negotiation and/or performance of oneor more particular activities among users/parties. For example, anexemplary smart contract may be configured to be partially or fullyself-executing and/or self-enforcing. In some embodiments, the exemplaryinventive asset-tokenized distributed blockchain-type ledgerimplementations of the present disclosure may utilize smart contractarchitecture that can be implemented by replicated asset registries andcontract execution using cryptographic hash chains and Byzantine faulttolerant replication. For example, each node in a peer-to-peer networkor blockchain distributed network may act as a title registry andescrow, thereby executing changes of ownership and implementing sets ofpredetermined rules that govern transactions on the network. Forexample, each node may also check the work of other nodes and in somecases, as noted above, function as miners or validators.

In some embodiments, the private and/or private-permissionedcryptographically-protected, distributed database and/or distributedledger may be implemented as a layer of the mesh network, such as, e.g.,as a tenant on the mesh network infrastructure. In some embodiments, theprivate and/or private-permissioned cryptographically-protected,distributed database and/or distributed ledger may be implemented usingthe mesh network and/or using a separate mesh network alongside the meshnetwork, such as, e.g., using a separate radio and/or transceiver ofeach integrated roofing mesh network gateway 100 in the mesh network.

In some embodiments, the data tracking engine 130 may maintain a datastructure to track data capacity through time. In some embodiments, thedata structure may include, e.g., a table, comma-separated-values (CSV),or other data structure that tallies consumed data capacity andpassthrough data capacity usage over a period of time (e.g., a billingperiod, or other suitable time period as described above). The datastructure may be stored, e.g., locally in the integrated roofing meshnetwork gateway 100, in a cloud service or centralized server and/ordatabase, or any combination thereof. Accordingly, in some embodiments,the data structure may be accessed across the mesh network 180 using,e.g., an API by, e.g., a billing engine, customer website, or anysuitable technical service or any combination thereof. As a result, thecustomer, the carrier, or other suitable entity may access the datastructure and the consumed data capacity and passthrough data capacityusage (e.g., the data capacity used by the customer and the datacapacity contributed by the customer to the mesh network 180).

In some embodiments, the data communication metric may define the amountof bandwidth used by the user device 160 via the consumed data capacityversus the amount of bandwidth added to the mesh network 180 by theintegrated roofing mesh network gateway 100 via the passthrough datacapacity. In some embodiments, the data communication metric may includethe magnitude of the passthrough data capacity according to thestatistical aggregation, a ratio of the magnitudes of the passthroughdata capacity and consumed data capacity, a ratio of the passthroughdata capacity to a total mesh network 180 bandwidth, or other suitablecharacterization measuring participation by the integrated roofing meshnetwork node 170 in the mesh network 180.

In some embodiments, the data tracking engine 130 may log thepassthrough data capacity, the consumed data capacity, and/or the datacommunication metric, e.g., in the storage device 101 and/or the RAM103. In some embodiments, a data model engine 140 may access the log ofthe passthrough data capacity, the consumed data capacity, and/or thedata communication metric to use a data communication prediction modelto learn patterns of the participation by the integrated roofing meshnetwork node 170 in the mesh network 180. Accordingly, the data modelengine 140 may predict the data communication metric for a next periodof time based on the patterns.

In some embodiments, the inventive computer-based systems/platforms, theinventive computer-based devices, and/or the inventive computer-basedcomponents of the present disclosure may be configured to utilize one ormore AI/machine learning techniques chosen from, but not limited to,decision trees, boosting, support-vector machines, neural networks,nearest neighbor algorithms, Naive Bayes, bagging, random forests, andthe like. In some embodiments and, optionally, in combination of anyembodiment described above or below, an neutral network technique may beone of, without limitation, feedforward neural network, radial basisfunction network, recurrent neural network, convolutional network (e.g.,U-net) or other suitable network. In some embodiments and, optionally,in combination of any embodiment described above or below, animplementation of Neural Network may be executed as follows:

-   -   a. define Neural Network architecture/model,    -   b. transfer the input data to the neural network model,    -   c. train the model incrementally,    -   d. determine the accuracy for a specific number of timesteps,    -   e. apply the trained model to process the newly-received input        data,    -   f. optionally and in parallel, continue to train the trained        model with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the trained neural network model may specify aneural network by at least a neural network topology, a series ofactivation functions, and connection weights. For example, the topologyof a neural network may include a configuration of nodes of the neuralnetwork and connections between such nodes. In some embodiments and,optionally, in combination of any embodiment described above or below,the trained neural network model may also be specified to include otherparameters, including but not limited to, bias values/functions and/oraggregation functions. For example, an activation function of a node maybe a step function, sine function, continuous or piecewise linearfunction, sigmoid function, hyperbolic tangent function, or other typeof mathematical function that represents a threshold at which the nodeis activated. In some embodiments and, optionally, in combination of anyembodiment described above or below, the aggregation function may be amathematical function that combines (e.g., sum, product, etc.) inputsignals to the node. In some embodiments and, optionally, in combinationof any embodiment described above or below, an output of the aggregationfunction may be used as input to the activation function. In someembodiments and, optionally, in combination of any embodiment describedabove or below, the bias may be a constant value or function that may beused by the aggregation function and/or the activation function to makethe node more or less likely to be activated.

In some embodiments, the integrated roofing mesh network gateway 100 mayprovide the data communication metric and/or the predicted datacommunication metric to one or more providers, e.g., via the meshnetwork 180. For example, in some embodiments, the integrated roofingmesh network gateway 100 may send data via the mesh network radio 105 asan outbound transmission addressed to the one or more providers. In someembodiments, the one or more providers may include an operator of thephysical infrastructure of the mesh network 180, an operator of avirtual mesh network that employs the physical infrastructure, or otherprovider or any combination thereof.

In some embodiments, a computing system associated with the provider(s)may use the data communication metric and/or predicted datacommunication metric to manage network routing configurations tooptimize the distribution of traffic across the mesh network 180, e.g.,using a data management engine or data management engine service orother hardware, software or combination thereof configured to managetraffic routing through the mesh network 180. In some embodiments, byreceiving the data communication metric and/or predicted datacommunication metric from each integrated roofing mesh network node 170on the mesh network 180, a provider may be provided with real-timeupdates to not only node performance and bandwidth usage, but also nodebandwidth contribution. For example, the data communication metricand/or predicted data communication metric may be recorded in a log ofthe provider or by another other means of notifying the provider of thedata communication metric and/or predicted data communication metric.

As a result, optimization functions defining the routing of traffic inthe mesh network 180 can be employed to optimize the routing of trafficbased on the distribution of bandwidth consumption across nodes, thedistribution of bandwidth contribution across nodes, the distribution ofa ratio of bandwidth consumption to bandwidth contribution, among othermeasurements of network capacity and network performance using the datacommunication metric and/or predicted data communication metric.Accordingly, based on the optimization functions, the provider mayconfigure the mesh network 180 to adjust routing such that bandwidthand/or bandwidth contribution are maximized.

In some embodiments, the optimization functions may include a costoptimization function. Users may be provided with incentives (e.g., cashrewards, rebates, discounts, etc.) for bandwidth contribution at theirintegrated roofing mesh network nodes 170. The value of the incentivesmay be an input to the optimization functions to minimize cost whilemaximizing performance of the mesh network 180. For example, the datacommunication metric and/or predicted data communication metric may beprovided to the provider to be redeemed for incentives. In someembodiments, for example, a token on a blockchain may be redeemed aspayment for, e.g., money, credit on a bill, additional data on a serviceplan (e.g., cellular data plan, cable plan, fiber optic plan, WiFihotspot plan, etc.), a gift card, a rebate on products and/or equipment,among other incentives or any combination thereof. In some embodiments,for example, the data communication metric may be exchanged by asuitable conversion methodology for, e.g., money, credit on a bill,additional data on a service plan (e.g., cellular data plan, cable plan,fiber optic plan, WiFi hotspot plan, etc.), a gift card, a rebate onproducts and/or equipment, among other incentives or any combinationthereof. For example, the data communication metric may include pointsor other quantity indicator to be redeemed for a monetary equivalent orproduct/services equivalent.

Alternatively, or in addition, in some embodiments, the integratedroofing mesh network gateway 100 may include a data management engine150 to manage data traffic and/or bandwidth utilization by theintegrated roofing mesh network node 170. Similar to a provider, thedata management engine 150 may use one or more optimization functions tooptimize communications by the integrated roofing mesh network node 170.For example, the data management engine 150 may use the datacommunication metric, the predicted data communication metric and anyapplicable incentives (e.g., according to an incentive structurespecifying incentives and incentive values according to the datacommunication metric) to maximize incentives and consumed data capacityperformance, maximize performance, or optimize according to any othersuitable prioritization parameter or any combination thereof.Accordingly, the data management engine 150 may utilize the datacommunication metric and/or the predicted data communication metric todetermine a prioritization parameter that defines a priority of trafficthrough the integrated roofing mesh network node 170 to prioritizepassthrough data packets, consumed data packets or any other datapackets or any combination thereof. The processor(s) 109 may thencontrol the integrated roofing mesh network gateway 100 via softwareinstructions to execute communications over the mesh network 180 in anorder and quantity according to the prioritization parameter.

In some embodiments, the integrated roofing mesh network node 170 mayhave a limited bandwidth to use for sending and receiving data via themesh network 180. As a result, there may be times when the passthroughdata and the consumed data exceed the available bandwidth of theintegrated roofing mesh network node 170. Thus, the optimizationfunction may apportion bandwidth of the integrated roofing mesh networknode 170 according to, e.g., a weighting of the passthrough data and theconsumed data.

In some embodiments, where incentives are provided for the passthroughdata and bandwidth contribution due to the passthrough data, theweighting of the passthrough data and the consumed data may bedetermined based on the incentives. For example, a user may select,e.g., via the user device 160, a target incentive value for a givenperiod. Based on the target incentive value, the data management engine150 may execute a prioritization of the routing of passthrough datarelative to the routing of consumed data, e.g., by adjusting theweighting. For example, where the target incentive value is increase,the weighting of the routing of the passthrough data may be increasedrelative to the weighting of the routing of the consumed data (e.g., byincreasing the weighting of the routing of the passthrough data,decreasing the weighting of the routing of the consumed data, or anycombination thereof). As a result, in some embodiments, in times of lowbandwidth (e.g., bandwidth available to the integrated roofing meshnetwork node 170 being below the bandwidth needed for all of thepassthrough data and the consumed data), the data management engine 150may prioritize, according to the weightings, the routing of futurepassthrough data or the routing of future non-passthrough dataassociated with communications that are not passthrough data packets,such as future consumed data.

FIG. 2 is a block diagram illustrating a structure having an integratedroofing mesh network gateway in accordance with one or more embodimentsof the present disclosure.

In some embodiments, the integrated roofing mesh network gateway 100 maybe connected to a mesh network radio 105 installed at a user premises,such as integrated into a roof 21 of a structure 20. In someembodiments, the structure 20 may include a residential structure, suchas house, townhouse, condominium or other residential structure. Thestructure 20 may include a commercial structure such as, e.g., an officebuilding, apartment building, store, warehouse, transportation-relatedstructure (e.g., train station, bus stop, airport, parking garage,etc.), or other roofed structure or any combination thereof.

In some embodiments, the integrated roofing mesh network gateway 100monitors inbound data packets 203 of inbound network traffic andoutbound data packets 208 of outbound network traffic passing throughthe mesh network radio 105. In some embodiments, the inbound datapackets 203 and outbound data packets 208 may be analyzed, and trafficsource and destination is attributed to either a known or unknownsource. In some embodiments, the total bandwidth consumed by each sourceis measured, and the user's consumption and external traffic consumptionmay be calculated.

In some embodiments, the integrated roofing mesh network gateway 100 mayreceive inbound data packets 203 from the inbound data packets 203received by the mesh network radio 105. Based on the addresses in eachinbound data packet 203, the integrated roofing mesh network gateway 100may either route each inbound data packet 203 to the user device 160 todeliver data requested by the user device 160, for route the inbounddata packets 203 externally, e.g., to another node, as outbound datapackets 207 for transmission with the outbound data packets 208.

In some embodiments, the inbound data packets 203 routed to the userdevice 160 may be conveyed as user consumed data packets 205 to the userdevice 160, e.g., via a wired or wireless connection between theintegrated roofing mesh network gateway 100 and the user device 160.Similarly, in some embodiments, the user device 160 may send data acrossthe mesh network by conveying user produced data packets 206 to theintegrated roofing mesh network gateway 100 for routing across the meshnetwork. In some embodiments, the integrated roofing mesh networkgateway 100 may receive the user produced data packets 206 andcommunicated the user produced data packets 206 to the mesh networkradio 105 with the outbound data packets 207 for transmission asoutbound data packets 208.

In some embodiments, the integrated roofing mesh network gateway 100 maymeasure the amount of bandwidth of the mesh network radio 105 consumedby the inbound data packets 203 and outbound data packets 207 associatedwith the user consumed data 204 and the user produced data packets 206to determine the consumed data capacity of the mesh network. In someembodiments, the integrated roofing mesh network gateway 100 may alsomeasure the amount of bandwidth of the mesh network radio 105 consumedby the inbound data packets 203 and outbound data packets 207 associatedwith neither the user consumed data 204 nor the user produced datapackets 206 to determine the passthrough data capacity of the meshnetwork representing the bandwidth added to the mesh network by theintegrated roofing mesh network gateway 100 and mesh network radio 105.

FIG. 3 is a block diagram illustrating a mesh network of integratedroofing mesh network gateways in accordance with one or more embodimentsof the present disclosure.

In some embodiments, the integrated roofing mesh network node 170 andthe integrated roofing mesh network gateway 100 can be installed on aplurality of roofs of a plurality of structures 300 so as to create anintegrated roofing accessory network (5G network). In some embodiments,a plurality of integrated roofing mesh network nodes 170 describedherein can be installed on a single roof so as to create the meshnetwork of integrated roofing mesh network nodes 170.

In some embodiments, a method of using an integrated roofing accessorynetwork described herein includes: providing a plurality of integratedroofing mesh network nodes 170 as described herein; transmitting atleast one electromagnetic signal 302 (e.g., a 5G signal) from a firstintegrated roofing mesh network node 170; and receiving the at least oneelectromagnetic signal 302 by a second integrated roofing mesh networknode 170. In some embodiments, the second integrated roofing meshnetwork node 170 further transmits the at least one electromagneticsignal 302 to a third integrated roofing mesh network node 170, and soon. In some embodiments, the first integrated roofing mesh network node170 is located on a first structure 300, the second integrated roofingmesh network node 170 is located on a second structure 300, the thirdintegrated roofing mesh network node 170 is located on a third structure300, and so on.

In some embodiments, the mesh network 180 may include multiple serviceproviders, each having a tenant virtual mesh network operating in amulti-tenancy arrangement on a common physical infrastructure formed bythe plurality integrated roofing mesh network nodes 170 of the pluralityof structures 300. In some embodiments, the integrated roofing meshnetwork nodes 170 across the mesh network 180 may define the physicalinfrastructure of the mesh network 180, and each service provider mayhave virtual networks layered on top of the mesh network 180 using thephysical infrastructure and connected to respective backhaul networksfor broader network coverage.

FIG. 4 illustrates a flowchart of an illustrative bandwidth trackingmethodology using the integrated roofing mesh network gateway inaccordance with one or more embodiments of the present disclosure.

In some embodiments, the integrated roofing mesh network gateway 100 maymonitor traffic 402 received and transmitted on the mesh network 180 bythe integrated roofing mesh network gateway 100. In some embodiments,the traffic 402 may include packets 403 carrying data according tocommunications instructed by devices on the mesh network 180. Eachpacket 403 may include a header 403 a and a payload 403 b. In someembodiments, the header 403 a may define a source address and adestination address of each packet while the payload 403 b may carry thedata being communicated according to the instruction by the devices onthe mesh network 180. In some embodiments, each packet 403 may betransmitted by the integrated roofing mesh network gateway 100 to anexternal destination address on the mesh network 180 from, e.g., theuser device 160 or other local device local to the integrated roofingmesh network gateway 100, received by the integrated roofing meshnetwork gateway 100 from an external source address on the mesh network180 to, e.g., the user device 160 or other local device local to theintegrated roofing mesh network gateway 100, or may be received by theintegrated roofing mesh network gateway 100 from an external sourceaddress on the mesh network 180 and then retransmitted to an externaldestination address on the mesh network 180. In some embodiments, thepacket routing engine 110 may control the integrated roofing meshnetwork gateway 100 and, e.g., the mesh network radio 105, to transmitand/or receive each packet according to the source and destinationaddress of each header 403 a.

In some embodiments, the packet routing engine 110 may also log therouting of the packets 403 where the packets 403 that have a sourceaddress and/or destination address of the user device 160 or other localdevice local to the integrated roofing mesh network gateway 100 may belogged as consumed traffic. In some embodiments, where the packets 403do not have a source address nor destination address associated with theuser device 160 or other local device local to the integrated roofingmesh network gateway 100 may be logged as passthrough traffic thatpasses through the integrated roofing mesh network gateway 100 as a partof the mesh network 180 routing. Accordingly, the passthrough traffic404 contributes to the communication capacity of the mesh network 180.

Accordingly, the packet tracking engine 120 may access the log of thepassthrough traffic 404 in order to monitor the contribution of theintegrated roofing mesh network gateway 100 to communications across themesh network 180. In some embodiments, the contributions may be measuredaccording to the size of the passthrough traffic, such as the data sizeof each packet 403, the throughput of packets 403 (e.g., data rate ofpayloads 403 b communicated), and/or according to the size of theconsumed traffic. Accordingly, the packet tracking engine 120 mayproduce and log the passthrough traffic size 405 based on the payloads403 b of the passthrough traffic 404 packets 403.

In some embodiments, a data tracking engine 130 may utilize thepassthrough traffic size 405 to measure the bandwidth usage attributableto the passthrough data. In some embodiments, the data tracking engine130 may formulate the data communication metric 406 for each serviceprovider according to the passthrough traffic size 405 for passthroughtraffic 404 of each service provider.

In some embodiments, the data tracking engine 130 may measure the datacommunication metric 406 over a period of time, such as, e.g., a billingperiod defined by a service provider, such as a service provider thatprovides data coverage via the mesh network 180. Accordingly, the datacommunication metric 406 may define, e.g., a sum, average, median, orother statistical aggregation of data communicated over the mesh networkthrough the integrated roofing mesh network node 170 (such as, e.g.,bandwidth) per time per service provider. In some embodiments, the datacommunicated may be measured as a type of bandwidth measurement, e.g.,goodput (e.g., bits communicated through the integrated roofing meshnetwork gateway 100 per time). In some embodiments, the time period mayinclude, e.g., a second, a minute, an hour, a day, a week, a month, orother suitable period of for defining consumed data capacity andpassthrough data capacity usage.

In some embodiments, the data communication metric 406 may define theamount of data communication capacity used by the user device 160 viathe consumed data capacity versus the amount of data capacity added tothe mesh network 180 by the integrated roofing mesh network gateway 100via the passthrough data capacity. In some embodiments, the datacommunication metric 406 may include the magnitude of the passthroughdata capacity according to the statistical aggregation, a ratio of themagnitudes of the passthrough data capacity and consumed data capacity,a ratio of the passthrough data capacity to a total mesh network 180bandwidth, or other suitable characterization measuring participation bythe integrated roofing mesh network node 170 in the mesh network 180.

In some embodiments, the data communication metric 406 may becommunicated to a computing device 407 associated with a serviceprovider and/or physical infrastructure provider. In some embodiments,the mesh network 180 may communicate control data and bearer data. Thebearer data may include data bearing traffic, such as the packets 403 ofthe traffic 402. The bearer traffic is related to the amount of datasent and received by each node on the mesh network 180, and thus has thegreatest effect on network capacity. Control data may include trafficassociated with reporting network performance and analytics, among otheroperational network information. Thus, in some embodiments, the datacommunication metric 406 may ignore control data to prevent theoperational network information from effecting the tracking of theparticipation by the integrated roofing mesh network gateway 100 in themesh network 180.

Accordingly, an administrator may access visualizations for the datacommunication metrics 406 for the integrated roofing mesh networkgateway 100 to identify the participation of the integrated roofing meshnetwork gateway 100 in the mesh network 180 and contribution to the meshnetwork capacity as a result of that participation. In some embodiments,the computing device 407 may produce visualizations such as graphs,tables, charts, and other presentations of the data communication metric406 for the integrated roofing mesh network gateway 100 for each periodof time. Additionally, in some embodiments, the computing device 407 maygenerate an incentive recommendation to instruct the rebate or reward ofthe contribution of the integrated roofing mesh network gateway 100 tothe mesh network 180. For example, in some embodiments, the computingdevice 407 may include an algorithm for generating a financial rewardbased on the data communication metric 406, such as, e.g., a reductionin a service bill, a rebate for additional data service, or otherfinancial incentive.

In some embodiments, the computing device 407 may generate a networkoptimization recommendation or instruction. In some embodiments, thecomputing device 407 may assess the data communication metric 406relative to the bandwidth available and/or used across the mesh network180 to determine whether the integrated roofing mesh network gateway 100experiences greater or less passthrough data capacity than other nodesin the mesh network 180. Based on the relate passthrough trafficaccording to the data communication metric 406 of the integrated roofingmesh network gateway 100, the computing device 407 may generate routingadjustments to better distribute network traffic.

FIG. 5 illustrates a flowchart of an illustrative packet routingtracking methodology for bandwidth tracking using the integrated roofingmesh network gateway in accordance with one or more embodiments of thepresent disclosure.

In some embodiments, the packet routing engine 110 may access eachpacket 403 passing through the integrated roofing mesh network gateway100. For example, the integrated roofing mesh network gateway 100 mayreceive data packets from the mesh network 180 and may transmit datapackets to the mesh network 180. Each of the received data packets andthe transmitted data packets may be accessed by the packet routingengine 110 to determine a route for each packet.

To do so, in some embodiments, the packet routing engine 110 mayexamine, at block 511, the packet headers of each packet of the receiveddata packets and the transmitted data packets. In some embodiments, thesource address and the destination address of each packet may beidentified and compared, at block 512, to an address associated with theintegrated roofing mesh network gateway 100, such as the user device 160or other device associated with the integrated roofing mesh networkgateway 100. In some embodiments, the source address and destinationaddress may include, e.g., unique identifiers across the mesh network180, although the mesh network 180 may allow for local, privateaddresses, or locally administered addresses that may not be unique. Insome embodiments, the mesh network 180 may utilize special networkaddresses that are allocated as broadcast or multicast addresses. Insome cases, nodes, such as the integrated roofing mesh network gateway100, may have more than one network address. For example, each networkinterface may be uniquely identified. Further, because protocols may belayered, more than one protocol's network address can occur in anyparticular network interface or node and more than one type of networkaddress may be used in any one network. In some embodiments, networkaddresses can be flat addresses which contain no information about thenode's location in the network (such as a MAC address) or may containstructure or hierarchical information for the routing (such as an IPaddress). Any suitable addressing scheme may be employed, such as, e.g.,telephone numbers on a public switched telephone network, internetprotocol (IP) addresses, Internetwork Packet Exchange (IPX) address, MACaddresses, X.25 addresses on a circuit switched network, X.21 addresseson a circuit switched network, or any other suitable address or anycombination thereof.

In some embodiments, upon identifying the source and destinationaddresses at block 512, each packet may be classified as a consumedpacket at block 513 or a passthrough packet at block 514. In someembodiments, where one of the source address or the destination addressof a packet matches the address associated with the node gateway, thepacket may be classified at block 513 as the consumed packet. In someembodiments, where neither the source address nor the destinationaddress of a packet matches the address associated with the nodegateway, the packet may be classified at block 514 as the passthroughpacket.

Accordingly, in some embodiments, the packets may be grouped into twosubsets of packets, a first subset of passthrough traffic 404 includingthe passthrough packets and a second subset of consumed traffic 504including the consumed packets. Thus, the packet routing engine 110 maymonitor the routing of packets via the integrated roofing mesh networkgateway 100 and track the passthrough traffic 404 and the consumedtraffic 504 of the integrated roofing mesh network node 170 based onwhether each packet is associated with data consumption by theintegrated roofing mesh network node 170.

FIG. 6 illustrates a flowchart of an illustrative packet payloadtracking methodology for bandwidth tracking using the integrated roofingmesh network gateway in accordance with one or more embodiments of thepresent disclosure.

In some embodiments, the packet tracking engine 120 may receive theconsumed traffic 504 and the passthrough traffic 404 to track packettraffic throughput to assess the data size of traffic. Accordingly, insome embodiments, examine a packet payload 403 b for each packet in theconsumed traffic 504 and for each packet in the passthrough traffic 404.In some embodiments, the amount of data in the payload 403 b of eachpacket defines the size of the passthrough traffic 404 and the consumedtraffic 504. For example, the payload 403 b may have a data size in,e.g., bits, kilobits, megabits, gigabits, bytes, kilobytes, megabytes,gigabytes, or other data size measurements.

Therefore, in some embodiments, the packet tracking engine 120 maydetermine, for each packet of the passthrough traffic 404 and theconsumed traffic 504 a payload 403 b size at block 622. In someembodiments, the size of the payload 403 b may defined, e.g., in theheader 403 a of each packet. In some embodiments, the packet trackingengine 120 may measure the payload 403 b size based on, e.g., a memoryfootprint used to store the packet and/or payload 403 b, such as, e.g.,in a buffer, cache, RAM and/or storage device. In some embodiments, thepacket tracking engine 120 may determine the payload 403 b size of eachpacket by counting a number of bits of the data contained within thepayload 403 b. Any other suitable method for determine a data size maybe employed.

In some embodiments, the packet tracking engine 120 may track the sizeof the payload of each of the passthrough traffic 404 and the consumedtraffic 504 upon transmission or reception by the integrated roofingmesh network gateway 100. Accordingly, the packet tracking engine 120may measure the consumed traffic throughput or goodput at block 623Aaccording to an amount of data communicated upon transmission orreception of a consumed data packet in the consumed traffic 504 and,thus, may output a consumed traffic size 605 according to the combinedthroughput of the consumed traffic 504. Similarly, the packet trackingengine 120 may measure the passthrough traffic throughput or goodput atblock 623B according to an amount of data communicated upon transmissionor reception of a passthrough data packet in the passthrough traffic 404and, thus, may output a passthrough traffic size 405 according to thecombined throughput of the passthrough traffic 404.

FIG. 7 illustrates a flowchart of an illustrative packet traffictracking methodology for bandwidth tracking using the integrated roofingmesh network gateway in accordance with one or more embodiments of thepresent disclosure.

In some embodiments, the data tracking engine 130 may analyze theconsumed traffic size 605 and the passthrough traffic size 405 todetermine a data communication metric 406 characterizing theparticipation of the integrated roofing mesh network gateway 100 in themesh network 180. Accordingly, in some embodiments, the data trackingengine 130 may track the traffic size of the consumed traffic size 605and the passthrough traffic size 405 through time at block 731.

In some embodiments, the data traffic and/or data capacity and/or datacommunication may be measure as bandwidth or by any other suitablemeasurement. In some embodiments, bandwidth is characterized as datasize per time (e.g., bits-per-second or bps). Bandwidth may include,e.g., bps, bits-per-minute, bits-per-hour, bits-per-day, bits-per-week,bits-per-month, bits-per-billing period, or the amount of bits over anyother suitable time period. In some embodiments, the billing period maybe a predefined length of time (e.g., one month, two months, threemonths, size months, one year, etc.), or may user selectable based onany suitable billing plan established between the user and a serviceprovider. Alternatively, the service provider may establish the billingperiod and define bandwidth as data per billing period. In someembodiments, the bandwidth as described above uses bits, however anyother measure of data size may be used, such as bytes, kilobits,kilobytes, etc.

Therefore, in some embodiments, by tracking traffic size through time atblock 731, the data tracking engine 130 may determine the consumed datacapacity and the passthrough data capacity by aggregating the trafficaccording to the time periods at block 732. For example, in someembodiments, the total or sum of the passthrough traffic size 405 overthe course of a billing period of a month may be determined based on thetotal payload size of all passthrough traffic within the billing periodto define the passthrough data capacity. Similarly, for example, in someembodiments, the total or sum of the consumed traffic size 605 over thecourse of a billing period of a month may be determined based on thetotal payload size of all consumed traffic within the billing period todefine the consumed data capacity. The traffic a size may be aggregatedas, e.g., a running total in each time period or may be summed upon eachtime period elapsing.

In some embodiments, the data tracking engine 130 may provide thepassthrough data capacity and/or the consumed data capacity to the datamodel engine 140 for input to a data communication prediction model. Insome embodiments, the data tracking engine 130 may detect and/or receiveadditional data associated with the passthrough data capacity and/or theconsumed data capacity may also be provided, such as, e.g., anidentifier of the time period (e.g., range of dates, range of times,communication completion date, communication completion time,communication commencement date, communication commencement time, or anyother suitable time period indicator or any combination thereof), asignal strength (e.g., decibel (dB) gain, returned signal strengthindicator (RSSI)) of the communication of each data packet, an averagesignal strength over the time period, a length of the time period,packet latency and a communication success rate, a communication failrate among other data or any combination thereof. Accordingly, the datamodel engine 140 may train the data communication prediction model topredict data communication capacity usage, either as consumed datacapacity, passthrough data capacity or both according to the time, date,signal strength, data packet headers, payload size, or other feature orany combination thereof.

In some embodiments, the data tracking engine 130 may use thepassthrough data capacity and/or the consumed data capacity to determinea metric at block 733 including a data communication metric 406 tocharacterize the participation of the integrated roofing mesh networkgateway 100 in the mesh network 180. In some embodiments, the metric maybe, e.g., the passthrough data capacity and/or the consumed datacapacity over the course of a time period, a variation of thepassthrough data capacity and/or the consumed data capacity of thecourse of time period (e.g., a variance or standard deviation), anaverage of the passthrough data capacity and/or the consumed datacapacity throughout the time period (e.g., on a per time basis, such asper second, per minute, per hour, per day, etc.) or any other suitabledata communication metric 406.

FIG. 8 illustrates a flowchart of an illustrative data communicationprediction machine learning model for data communication and capacitytracking using the integrated roofing mesh network gateway in accordancewith one or more embodiments of the present disclosure.

In some embodiments, the data model engine 140 may utilize the datacommunication prediction model 842 to predict a data communicationprediction 803 for the data communication metric N 801, e.g., the datacommunication metric 406 as described above. In some embodiments, thedata communication metric N 801 may include a historical datacommunication metric 406 determined and logged by the data trackingengine 130 as well as additional seasonality and externality data. As aresult, the data communication metric N 801 may include a datacommunication metric 406 for a historical period for which there is asubsequent historical period. In some embodiments, because the datatracking engine 130 determines and logs the data communication metric406 for each time period, a data communication metric N+1 802 may alsobe available for a next period of time, such as the subsequenthistorical period. Thus, a data communication prediction 803 may beproduced as an estimate or prediction of the data communication metricfor a next period of time relative to the data communication metric N801 can be compared against the logged data communication metric N+1 802that has been logged for the same next time period relative to the datacommunication metric N 801.

In some embodiments, the data communication prediction model 842 ingeststhe data communication metric N 801 and additional seasonality andexternality data, and produces a prediction of a data communicationprediction 803 for each data communication metric N 801. In someembodiments, to produce this prediction, the data communicationprediction model 842 may include a machine learning model including aregression and/or neural network model, such as, e.g., a recurrentneural network (CNN), linear regression, decision trees, random forest,support vector machine (SVM), K-Nearest Neighbors, or any other suitablealgorithm for quantitative prediction.

In some embodiments, upon training the data communication predictionmodel 842, the data communication prediction model 842 may be used togenerate predictions regarding data communication metrics, includingfuture data communication predictions. For example, in some embodiments,the data communication prediction 803 may be a prediction of networkload to and/or through a specific node and/or user device. Thus, bytraining the data communication prediction model 842 with previous usagepatterns as quantified by a data communication metric N 801, the datacommunication prediction model 842 may predict a future network loadbased on a current data communication metric.

In some embodiments, the data communication metric N 801 may includedata regarding external factors, such as heat, humidity, moisturecontent, weather (e.g., rain, snow, lightning, etc.) or otherenvironmental and/or external factors or any combination thereof. Forexample, the data communication metric N 801 may include a featurevector encoding the data communication metric with the external factorsor other information or any combination thereof. By training the datacommunication prediction model 842 with historical data communicationmetrics N 801 including external factors, the data communicationprediction model 842 may be sued to predict future network performanceat one or more particular nodes based on the external factors.

In some embodiments, the data communication metric N 801 may includetime and/or date data (e.g., time of day, day of the week, date, year,season, etc.). For example, the data communication metric N 801 mayinclude a feature vector encoding the data communication metric with thetime of day, date, day of the week, or other temporal information or anycombination thereof. By training the data communication prediction model842 with historical data communication metrics N 801 including temporalinformation, the data communication prediction model 842 may be sued topredict future network performance at one or more particular nodes basedon the temporal information.

In some embodiments, the data communication metric N 801 may includeevent data, such as, e.g., the occurrence of holidays, third-partyevents, the location of third-party events, commercial activities (e.g.,streaming TV show and movie releases, online commerce promotions andsales, video game events, live sports events, etc.), including a time,date and/or location thereof. For example, the data communication metricN 801 may include a feature vector encoding the data communicationmetric with event data. By training the data communication predictionmodel 842 with historical data communication metrics N 801 includingevent data, the data communication prediction model 842 may be sued topredict future network performance at one or more particular nodes basedon the event data of one or more expected events.

In some embodiments, the data communication metric N 801 may includenode density data, such as a number of nodes in a given area, a numberof nodes in communication with a particular node, a number of nodes onthe mesh network, or other suitable node density data. The node densitydata may vary with time, such as, e.g., user devices in an areaincreasing due to increased traffic or an event. Thus, the node densitydata may have predictive power for the data communication prediction803. Thus, the data communication prediction model 842 may be trainedbased on the node density data. For example, the data communicationmetric N 801 may include a feature vector encoding the datacommunication metric with node density data. By training the datacommunication prediction model 842 with historical data communicationmetrics N 801 including node density data, the data communicationprediction model 842 may be used to predict future network performanceat one or more particular nodes based on the node density data.

Accordingly, the data communication prediction model 842 ingests a datacommunication metric N 801 and processes the attributes encoded thereinusing the prediction model, such as a neural network, to produce a modeloutput vector. In some embodiments, the model output including the datacommunication prediction 803 such as, e.g., a next time period datacommunication metric prediction, a next time period passthrough datacapacity usage prediction, a next time period consumed data capacityusage prediction, or other data communication prediction.

In some embodiments, where the data communication prediction 803includes a prediction of a future data communication metric or otherdata communication prediction or combination thereof based on the datacommunication metric N 801. In some embodiments, the data communicationprediction 803 may be provided to the computing device 407, e.g., as aprediction of the next time period data communication metric predictiondescribed above. In some embodiments, the data communication prediction803 of the next time period data communication metric prediction maytrigger the computing device 407 to generate an automated instructionand/or recommendation for optimizing network traffic and/or network costby predictively distributing traffic routing across the mesh network 180for more evenly distributed traffic routing, data communication capacityaddition via node participation, and minimization of total incentivesprovided to nodes.

In some embodiments, the data communication prediction model 842 maytrained based on the data communication prediction 803 and a datacommunication metric N+1 802 logged for a next historical time periodimmediately following the historical time period of data communicationmetric N 801. Based on the difference between the data communicationprediction 803 and the data communication metric N+1 802, the parametersof the data communication prediction model 842 may be updated to improvethe accuracy of the data communication prediction 803.

In some embodiments, training is performed using the optimizer 844. Insome embodiments, the data communication prediction 803 fed back to theoptimizer 844. The optimizer 844 may also ingest the data communicationmetric N+1 802. In some embodiments, in the case of a data communicationprediction model 842 include a neural network, support vector machine orsimilar, the optimizer 844 may employ a loss function, such as, e.g.,Hinge Loss, Multi-class SVM Loss, Cross Entropy Loss, Negative LogLikelihood, or other suitable loss function. The loss functiondetermines an error of the data communication prediction 803 based onthe data communication metric N+1 802 and the data communication metricN 801. In some embodiments, the optimizer 844 may, e.g., backpropagatethe error to the data communication prediction model 842 to update theparameters using, e.g., gradient descent, heuristic, convergence orother optimization techniques and combinations thereof.

In some embodiments, the optimizer 844 may therefore train theparameters of the data communication prediction model 842 in anunsupervised fashion to approximate bandwidth usage patterns based onhistorical data communication metrics.

FIG. 9 depicts a block diagram of an computer-based system and platform900 in accordance with one or more embodiments of the presentdisclosure. However, not all of these components may be required topractice one or more embodiments, and variations in the arrangement andtype of the components may be made without departing from the spirit orscope of various embodiments of the present disclosure. In someembodiments, the illustrative computing devices and the illustrativecomputing components of the computer-based system and platform 900 maybe configured to manage a large number of members and concurrenttransactions, as detailed herein. In some embodiments, thecomputer-based system and platform 900 may be based on a scalablecomputer and network architecture that incorporates varies strategiesfor assessing the data, caching, searching, and/or database connectionpooling. An example of the scalable architecture is an architecture thatis capable of operating multiple servers.

In some embodiments, referring to FIG. 9 , an integrated roofing meshnetwork node 170(1), integrated roofing mesh network node 170(2) throughintegrated roofing mesh network node 170(n) of the computer-based systemand platform 900 may include virtually any computing device capable ofreceiving and sending a message over a mesh network (e.g., cloudnetwork), such as network 905, to and from another computing device,such as servers 906 and 907, each other, and the like. In someembodiments, the integrated roofing mesh network nodes 170(1) through170(n) may include personal computers, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PCs,and the like. In some embodiments, one or more of the integrated roofingmesh network nodes 170(1) through 170(n) may include integrated roofingmesh network gateways 100, mesh network radios 105, computing devicesthat typically connect using a wireless communications medium such ascell phones, smart phones, pagers, walkie talkies, radio frequency (RF)devices, infrared (IR) devices, GB-s citizens band radio, integrateddevices combining one or more of the preceding devices, or virtually anymobile computing device, and the like. In some embodiments, one or moreof the integrated roofing mesh network nodes 170(1) through 170(n) mayinclude devices that are capable of connecting using a wired or wirelesscommunication medium such as a PDA, POCKET PC, wearable computer, alaptop, tablet, desktop computer, a netbook, a video game device, apager, a smart phone, an ultra-mobile personal computer (UMPC), and/orany other device that is equipped to communicate over a wired and/orwireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, OFDM, OFDMA, LTE, satellite, ZigBee, CBRS,LoRa, etc.). In some embodiments, one or more of the integrated roofingmesh network nodes 170(1) through 170(n) may include one or more devicesand/or components that may run one or more applications, such asInternet browsers, mobile applications, voice calls, video games,videoconferencing, and email, among others. In some embodiments, one ormore of the integrated roofing mesh network nodes 170(1) through 170(n)may include one or more devices and/or components configured to receiveand to send web pages, and the like. In some embodiments, anspecifically programmed browser application of the present disclosuremay be configured to receive and display graphics, text, multimedia, andthe like, employing virtually any web based language, including, but notlimited to Standard Generalized Markup Language (SMGL), such asHyperText Markup Language (HTML), a wireless application protocol (WAP),a Handheld Device Markup Language (HDML), such as Wireless MarkupLanguage (WML), WMLScript, XML, JavaScript, and the like. In someembodiments, one or more of the integrated roofing mesh network nodes170(1) through 170(n) may include one or more devices and/or componentsspecifically programmed by either Java, .Net, QT, C, C++, Python, PHPand/or other suitable programming language. In some embodiment of thedevice software, device control may be distributed between multiplestandalone applications. In some embodiments, softwarecomponents/applications can be updated and redeployed remotely asindividual units or as a full software suite. In some embodiments, oneor more of the integrated roofing mesh network nodes 170(1) through170(n) may periodically report status or send alerts over text or email.In some embodiments, one or more of the integrated roofing mesh networknodes 170(1) through 170(n) may include a data recorder which isremotely downloadable by the user using network protocols such as FTP,SSH, or other file transfer mechanisms. In some embodiments, one or moreof the integrated roofing mesh network nodes 170(1) through 170(n) mayprovide several levels of user interface, for example, advance user,standard user. In some embodiments, one or more of the integratedroofing mesh network nodes 170(1) through 170(n) may include one or moredevices and/or components specifically programmed include or execute anapplication to perform a variety of possible tasks, such as, withoutlimitation, messaging functionality, browsing, searching, playing,streaming or displaying various forms of content, including locallystored or uploaded messages, images and/or video, and/or games.

In some embodiments, the network 905 may provide network access, datatransport and/or other services to any computing device coupled to it.In some embodiments, the network 905 may include and implement at leastone specialized network architecture that may be based at least in parton one or more standards set by, for example, without limitation, GlobalSystem for Mobile communication (GSM) Association, the InternetEngineering Task Force (IETF), and the Worldwide Interoperability forMicrowave Access (WiMAX) forum. In some embodiments, the network 905 mayimplement one or more of a GSM architecture, a General Packet RadioService (GPRS) architecture, a Universal Mobile TelecommunicationsSystem (UMTS) architecture, and an evolution of UMTS referred to as LongTerm Evolution (LTE). In some embodiments, the network 905 may includeand implement, as an alternative or in conjunction with one or more ofthe above, a WiMAX architecture defined by the WiMAX forum. In someembodiments and, optionally, in combination of any embodiment describedabove or below, the network 905 may also include, for instance, at leastone of a local area network (LAN), a wide area network (WAN), theInternet, a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtualprivate network (VPN), an enterprise IP network, or any combinationthereof. In some embodiments and, optionally, in combination of anyembodiment described above or below, at least one computer networkcommunication over the network 905 may be transmitted based at least inpart on one of more communication modes such as but not limited to: NFC,RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, OFDM, OFDMA, LTE, satellite and any combinationthereof. In some embodiments, the network 905 may also include massstorage, such as network attached storage (NAS), a storage area network(SAN), a content delivery network (CDN) or other forms of computer ormachine readable media.

In some embodiments, the server 906 or the server 907 may be a webserver (or a series of servers) running a network operating system,examples of which may include but are not limited to Apache on Linux orMicrosoft IIS (Internet Information Services). In some embodiments, theserver 906 or the server 907 may be used for and/or provide cloud and/ornetwork computing, containerized applications, headless applications,virtual machine functionality, etc. Although not shown in FIG. 9 , insome embodiments, the server 906 or the server 907 may have connectionsto external systems like email, SMS messaging, text messaging, adcontent providers, etc. Any of the features of the server 906 may bealso implemented in the server 907 and vice versa.

In some embodiments, one or more of the servers 906 and 907 may bespecifically programmed to perform, in non-limiting example, asauthentication servers, search servers, email servers, social networkingservices servers, Short Message Service (SMS) servers, Instant Messaging(IM) servers, Multimedia Messaging Service (MMS) servers, exchangeservers, photo-sharing services servers, advertisement providingservers, financial/banking-related services servers, travel servicesservers, or any similarly suitable service-base servers for users of theintegrated roofing mesh network nodes 170(1) through 170(n).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for example, one or more computing memberdevices 902-904, the server 906, and/or the server 907 may include aspecifically programmed software module that may be configured to send,process, and receive information using a scripting language, a remoteprocedure call, an email, a tweet, Short Message Service (SMS),Multimedia Message Service (MMS), instant messaging (IM), an applicationprogramming interface, Simple Object Access Protocol (SOAP) methods,Common Object Request Broker Architecture (CORBA), HTTP (HypertextTransfer Protocol), REST (Representational State Transfer), SOAP (SimpleObject Transfer Protocol), MLLP (Minimum Lower Layer Protocol), or anycombination thereof.

FIG. 10 depicts a block diagram of another computer-based system andplatform 1000 in accordance with one or more embodiments of the presentdisclosure. However, not all of these components may be required topractice one or more embodiments, and variations in the arrangement andtype of the components may be made without departing from the spirit orscope of various embodiments of the present disclosure. In someembodiments, the member computing device 1002 a, member computing device1002 b through member computing device 1002 n shown each at leastincludes a computer-readable medium, such as a random-access memory(RAM) 1008 coupled to a processor 1010 or FLASH memory. In someembodiments, the processor 1010 may execute computer-executable programinstructions stored in memory 1008. In some embodiments, the processor1010 may include a microprocessor, an ASIC, and/or a state machine. Insome embodiments, the processor 1010 may include, or may be incommunication with, media, for example computer-readable media, whichstores instructions that, when executed by the processor 1010, may causethe processor 1010 to perform one or more steps described herein. Insome embodiments, examples of computer-readable media may include, butare not limited to, an electronic, optical, magnetic, or other storageor transmission device capable of providing a processor, such as theprocessor 1010 of member computing device 1002 a, with computer-readableinstructions. In some embodiments, other examples of suitable media mayinclude, but are not limited to, a floppy disk, CD-ROM, DVD, magneticdisk, memory chip, ROM, RAM, an ASIC, a configured processor, alloptical media, all magnetic tape or other magnetic media, or any othermedium from which a computer processor can read instructions. Also,various other forms of computer-readable media may transmit or carryinstructions to a computer, including a router, private or publicnetwork, or other transmission device or channel, both wired andwireless. In some embodiments, the instructions may comprise code fromany computer-programming language, including, for example, C, C++,Visual Basic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, member computing devices 1002 a through 1002 n mayalso comprise a number of external or internal devices such as a mouse,a CD-ROM, DVD, a physical or virtual keyboard, a display, or other inputor output devices. In some embodiments, examples of member computingdevices 1002 a through 1002 n (e.g., clients) may be any type ofprocessor-based platforms that are connected to a network 1006 such as,without limitation, personal computers, digital assistants, personaldigital assistants, smart phones, pagers, digital tablets, laptopcomputers, Internet appliances, and other processor-based devices. Insome embodiments, member computing devices 1002 a through 1002 n may bespecifically programmed with one or more application programs inaccordance with one or more principles/methodologies detailed herein. Insome embodiments, member computing devices 1002 a through 1002 n mayoperate on any operating system capable of supporting a browser orbrowser-enabled application, such as Microsoft™, Windows™, and/or Linux.In some embodiments, member computing devices 1002 a through 1002 nshown may include, for example, personal computers executing a browserapplication program such as Microsoft Corporation's Internet Explorer™,Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/or Opera.

In some embodiments, through the member computing client devices 1002 athrough 1002 n, user 1012 a, user 1012 b through user 1012 n, maycommunicate over the network 1006 with each other and/or with othersystems and/or devices coupled to the network 1006 using one or moreintegrated roofing mesh network nodes, such as the integrated roofingmesh network nodes 170(1) through 170(n) described above. As shown inFIG. 10 , server devices 1004 and 1013 may include processor 1005 andprocessor 1014, respectively, as well as memory 1017 and memory 1016,respectively. In some embodiments, the server devices 1004 and 1013 maybe also coupled to the network 1006. In some embodiments, one or moremember computing devices 1002 a through 1002 n may be mobile clients.

In some embodiments, at least one database of databases 1007 and 1015may be any type of database, including a database managed by a databasemanagement system (DBMS). In some embodiments, an DBMS-managed databasemay be specifically programmed as an engine that controls organization,storage, management, and/or retrieval of data in the respectivedatabase. In some embodiments, the DBMS-managed database may bespecifically programmed to provide the ability to query, backup andreplicate, enforce rules, provide security, compute, perform change andaccess logging, and/or automate optimization. In some embodiments, theDBMS-managed database may be chosen from Oracle database, IBM DB2,Adaptive Server Enterprise, FileMaker, Microsoft Access, Microsoft SQLServer, MySQL, PostgreSQL, and a NoSQL implementation. In someembodiments, the DBMS-managed database may be specifically programmed todefine each respective schema of each database in the DBMS, according toa particular database model of the present disclosure which may includea hierarchical model, network model, relational model, object model, orsome other suitable organization that may result in one or moreapplicable data structures that may include fields, records, files,and/or objects. In some embodiments, the DBMS-managed database may bespecifically programmed to include metadata about the data that isstored.

In some embodiments, the inventive computer-based systems/platforms, theinventive computer-based devices, and/or the inventive computer-basedcomponents of the present disclosure may be specifically configured tooperate in a cloud computing architecture 1025 such as, but not limitingto: infrastructure a service (IaaS) 1210, platform as a service (PaaS)1208, and/or software as a service (SaaS) 1206 using a web browser,mobile app, thin client, terminal emulator or other endpoint 1204. Insuch a cloud computing architecture 1025, functionality and/or softwarecomponents of the integrated roofing mesh network gateway 100, such asfor the packet routing engine 110, the packet tracking engine 120, thedata tracking engine 130, the data model engine 140, the data managementengine 150, among other computer engines and functions, may be providedas a software service provided by the cloud computing architecture 1025,e.g., using the SaaS 1206 layer. Accordingly, in some embodiments, theintegrated roofing mesh network gateway 100 may send records of inboundand outbound data packets, data packet addresses, data packet payloadsizes, or other data or any combination thereof to the cloud computingarchitecture 1025. The cloud computing architecture 1025 may instantiateone or more of the packet routing engine 110, the packet tracking engine120, the data tracking engine 130, the data model engine 140, and thedata management engine 150 as a cloud service to provide thefunctionality for determine a data communication metric, predicted datacommunication metric, data communication management scheme, or anycombination thereof to the integrated roofing mesh network node 170.FIGS. 11 and 12 illustrate schematics of implementations of the cloudcomputing/architecture(s) in which the inventive computer-basedsystems/platforms, the inventive computer-based devices, and/or theinventive computer-based components of the present disclosure may bespecifically configured to operate.

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

In some embodiments, the mesh network described herein may include anysuitable distributed network environment, communicating with one anotherover one or more suitable data communication networks (e.g., theInternet, satellite, etc.) and utilizing one or more suitable datacommunication protocols/modes such as, without limitation, IPX/SPX,X.25, AX.25, AppleTalk™, TCP/IP (e.g., HTTP), near-field wirelesscommunication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G,4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and othersuitable communication modes.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

Computer-related systems, computer systems, and systems, as used herein,include any combination of hardware and software. Examples of softwaremay include software components, programs, applications, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computer code,computer code segments, words, values, symbols, or any combinationthereof. Determining whether an embodiment is implemented using hardwareelements and/or software elements may vary in accordance with any numberof factors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores,” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, one or more of illustrative computer-based systemsor platforms of the present disclosure may include or be incorporated,partially or entirely into at least one personal computer (PC), laptopcomputer, ultra-laptop computer, tablet, touch pad, portable computer,handheld computer, palmtop computer, personal digital assistant (PDA),cellular telephone, combination cellular telephone/PDA, television,smart device (e.g., smart phone, smart tablet or smart television),mobile internet device (MID), messaging device, data communicationdevice, and so forth.

As used herein, term “server” should be understood to refer to a servicepoint which provides processing, database, and communication facilities.By way of example, and not limitation, the term “server” can refer to asingle, physical processor with associated communications and datastorage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud servers are examples.

In some embodiments, as detailed herein, one or more of thecomputer-based systems of the present disclosure may be implementedacross one or more of various computer platforms such as, but notlimited to: (1) FreeBSD, NetBSD, OpenBSD; (2) Linux; (3) MicrosoftWindows™; (4) OpenVMS™; (5) OS X (MacOS™); (6) UNIX™; (7) Android; (8)iOS™; (9) Embedded Linux; (10) Tizen™; (11) WebOS™; (12) Adobe AIR™;(13) Binary Runtime Environment for Wireless (BREW™); (14) Cocoa™ (API);(15) Cocoa™ Touch; (16) Java™ Platforms; (17) JavaFX™; (18) QNX™; (19)Mono; (20) Google Blink; (21) Apple WebKit; (22) Mozilla Gecko™; (23)Mozilla XUL; (24) .NET Framework; (25) Silverlight™; (26) Open WebPlatform; (27) Oracle Database; (28) Qt™; (29) SAP NetWeaver™; (30)Smartface™; (31) Vexi™; (32) Kubernetes™ and (33) Windows Runtime(WinRT™) or other suitable computer platforms or any combinationthereof. In some embodiments, illustrative computer-based systems orplatforms of the present disclosure may be configured to utilizehardwired circuitry that may be used in place of or in combination withsoftware instructions to implement features consistent with principlesof the disclosure. Thus, implementations consistent with principles ofthe disclosure are not limited to any specific combination of hardwarecircuitry and software. For example, various embodiments may be embodiedin many different ways as a software component such as, withoutlimitation, a stand-alone software package, a combination of softwarepackages, or it may be a software package incorporated as a “tool” in alarger software product.

In some embodiments, illustrative computer-based systems or platforms ofthe present disclosure may be configured to output to distinct,specifically programmed graphical user interface implementations of thepresent disclosure (e.g., a desktop, a web app., etc.). In variousimplementations of the present disclosure, a final output may bedisplayed on a displaying screen which may be, without limitation, ascreen of a computer, a screen of a mobile device, or the like. Invarious implementations, the display may be a holographic display. Invarious implementations, the display may be a transparent surface thatmay receive a visual projection. Such projections may convey variousforms of information, images, or objects. For example, such projectionsmay be a visual overlay for a mobile augmented reality (MAR)application.

In some embodiments, illustrative computer-based systems or platforms ofthe present disclosure may be configured to be utilized in variousapplications which may include, but not limited to, gaming,mobile-device games, video chats, video conferences, live videostreaming, video streaming and/or augmented reality applications,mobile-device messenger applications, and others similarly suitablecomputer-device applications.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, PersonalDigital Assistant (PDA), Blackberry™, Pager, Smartphone, or any otherreasonable mobile electronic device.

In some embodiments, the illustrative computer-based systems orplatforms of the present disclosure may be configured to securely storeand/or transmit data by utilizing one or more of encryption techniques(e.g., private/public key pair, Triple Data Encryption Standard (3DES),block cipher algorithms (e.g., IDEA, RC2, RC5, CAST and Skipjack),cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTR0, SHA-1,SHA-2, Tiger (TTH), WHIRLPOOL, RNGs).

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber” “consumer” or“customer” should be understood to refer to a user of an application orapplications as described herein and/or a consumer of data supplied by adata provider. By way of example, and not limitation, the terms “user”or “subscriber” can refer to a person who receives data provided by thedata or service provider over the Internet in a browser session, or canrefer to an automated software application which receives the data andstores or processes the data.

The aforementioned examples are, of course, illustrative and notrestrictive.

At least some aspects of the present disclosure will now be describedwith reference to the following numbered clauses.

-   1. A method comprising:    -   receiving, by a processor of a gateway of an integrated roofing        mesh network node in a mesh network of other nodes, a plurality        of received data packets from the mesh network;    -   transmitting, by the processor, a plurality of transmitted data        packets to the mesh network;        -   wherein each data packet of the plurality of received data            packets and the plurality of transmitted data packets            comprises:            -   i) a source address of a sending node,            -   ii) a destination address of a receiving node, and            -   iii) a payload of data;    -   comparing, by the processor, the source address and the        destination address of each data packet with an address        associated with the gateway;    -   determining, by the processor, passthrough traffic based at        least in part on:        -   i) the address associated with the gateway, and        -   ii) the source address and the destination address of each            data packet;        -   wherein the passthrough traffic comprises a subset of the            plurality of received data packets and the plurality of the            transmitted data packets that is routed between two or more            radio nodes of the mesh network through the gateway of the            integrated roofing mesh network node based at least in part            on the source address and the destination address of each            data packet;    -   determining, by the processor, a passthrough data capacity based        at least in part on the payload of data of each data packet in        the subset;    -   determining, by the processor, a metric based at least in part        on the passthrough data capacity; and    -   communicating, by the processor, the metric to service provider        to notify the service provider of an amount of mesh network        bandwidth provided by the passthrough data capacity of the        integrated roofing mesh network node.-   2. A system comprising:    -   a gateway of an integrated roofing mesh network node in        communication with a mesh network of other nodes;    -   wherein the gateway comprises a processor configured to execute        software instructions that cause the processor to perform steps        to:        -   receive a plurality of received data packets from the mesh            network;        -   transmit a plurality of transmitted data packets to the mesh            network;            -   wherein each data packet of the plurality of received                data packets and the plurality of transmitted data                packets comprises:                -   i) a source address of a sending node,                -   ii) a destination address of a receiving node, and                -   iii) a payload of data;        -   compare the source address and the destination address of            each data packet with an address associated with the            gateway;        -   determine passthrough traffic based at least in part on:            -   i) the address associated with the gateway, and            -   ii) the source address and the destination address of                each data packet;            -   wherein the passthrough traffic comprises a subset of                the plurality of received data packets and the plurality                of the transmitted data packets that is routed between                two or more radio nodes of the mesh network through the                gateway of the integrated roofing mesh network node                based at least in part on the source address and the                destination address of each data packet;        -   determine a passthrough data capacity based at least in part            on the payload of data of each data packet in the subset;        -   determine a metric based at least in part on the passthrough            data capacity; and        -   communicate the metric to service provider to notify the            service provider of an amount of mesh network bandwidth            provided by the passthrough data capacity of the integrated            roofing mesh network node.-   3. A method comprising:    -   receiving, by a processor of a gateway of an integrated roofing        mesh network node in a mesh network of other nodes, a data        packet associated with the mesh network;        -   wherein the data packet comprises:            -   i) a header specifying:                -   a virtual mesh network identifier identifying a                    virtual mesh network operating as a tenant of the                    mesh network,                -   a source address of a sending node, and                -   a destination address of a receiving node, and            -   iii) a payload of data;    -   identifying, by the processor, the data packet as passthrough        traffic based at least in part on:        -   i) the address associated with the gateway, and        -   ii) the address and the destination address of the data            packet;        -   wherein the passthrough traffic comprises data traffic that            is routed between two or more radio nodes of the mesh            network through the gateway of the integrated roofing mesh            network node based at least in part on the source address            and the destination address of the data packet;    -   determining, by the processor, a passthrough data capacity based        at least in part on the payload of data of the data packet;    -   determining, by the processor, a service provider of the mesh        network based at least in part on the virtual mesh network        identifier;    -   determining, by the processor, a service provider-specific        metric based at least in part on the passthrough data capacity        and the service provider of the mesh network; and    -   communicating, by the processor, the metric to the service        provider to notify the service provider of an amount of mesh        network bandwidth provided by the passthrough data capacity of        the integrated roofing mesh network node.-   4. The systems and/or methods as recited in any of clauses 1 through    3, further comprising:    -   determining, by the processor, consumed traffic based at least        in part on:        -   i) the address associated with the processor, and        -   ii) the source address and the destination address of each            data packet;        -   wherein the consumed traffic comprises a second subset of            the plurality of received data packets and the plurality of            the transmitted data packets that is routed between the            integrated roofing mesh network node and radio node of the            mesh network based at least in part on the source address            and the destination address of each data packet;    -   determining, by the processor, a consumed data capacity based at        least in part on the payload of data of each data packet in the        second subset; and    -   determining, by the processor, the metric based at least in part        on the passthrough data capacity and the consumed data capacity.-   5. The systems and/or methods as recited in clause 4, wherein the    metric comprises a ratio of the passthrough data capacity to the    consumed data capacity.-   6. The systems and/or methods as recited in any of clauses 1 through    3, further comprising determining, by the processor, a size of the    payload of data of each data packet in the subset.-   7. The systems and/or methods as recited in clause 6, wherein the    passthrough data capacity comprises a sum of the size of the payload    of data of each data packet in the subset over a first period of    time.-   8. The systems and/or methods as recited in any of clauses 1 through    3, further comprising:    -   determining, by the processor, a data communication        prioritization parameter based at least in part on the        passthrough data capacity;        -   wherein the data communication prioritization parameter            comprises relative priority of communication of the            passthrough data traffic and non-passthrough data traffic;            and    -   instructing, by the processor, the gateway to prioritize        communication of a plurality of future received data packets and        a plurality of future transmitted data packets based at least in        part on the data communication prioritization parameter.-   9. The systems and/or methods as recited in any of clauses 1 through    3, further comprising:    -   determining, by the processor, a tenant mesh network associated        with each data packet in the subset;        -   wherein the mesh network of radio nodes comprises a physical            infrastructure layer;        -   wherein a service layer utilizes the physical infrastructure            layer for data service, the service layer comprising a            plurality of tenant mesh networks sharing the mesh network            of the physical infrastructure layer;    -   determining, by the processor, the passthrough data capacity        associated with the tenant mesh network based at least in part        on the payload of data of each data packet associated with the        tenant mesh network in the subset;    -   determining, by the processor, tenant-specific metric based at        least in part on the passthrough data capacity; and    -   communicating, by the processor, the tenant-specific metric to a        service provider associated with the tenant mesh network.-   10. The systems and/or methods as recited in any of clauses 1    through 3, further comprising:    -   detecting, by the processor, a signal strength of the integrated        roofing mesh network node with each radio node of the mesh        network; and    -   utilizing, by the processor, a data communication prediction        machine learning model to estimate a consumed data capacity for        a next period of time;        -   wherein the consumed data capacity comprises a second subset            of the plurality of received data packets and the plurality            of the transmitted data packets that is routed between the            integrated roofing mesh network node and radio node of the            mesh network based at least in part on the source address            and the destination address of each data packet.-   11. The systems and/or methods as recited in clause 10, wherein the    mesh network comprises a fifth generation cellular (5G) network, the    integrated roofing mesh network node comprises an integrated 5G    radio.-   12. The systems and/or methods as recited in any of clauses 1    through 3, wherein the mesh network comprises a physical    infrastructure layer comprising of the integrated roofing mesh    network node and the other nodes;    -   wherein the mesh network comprises a multi-tenancy virtual        network layer having a plurality of virtual mesh networks.

Publications cited throughout this document are hereby incorporated byreference in their entirety. While one or more embodiments of thepresent disclosure have been described, it is understood that theseembodiments are illustrative only, and not restrictive, and that manymodifications may become apparent to those of ordinary skill in the art,including that various embodiments of the inventive methodologies, theillustrative systems and platforms, and the illustrative devicesdescribed herein can be utilized in any combination with each other.Further still, the various steps may be carried out in any desired order(and any desired steps may be added and/or any desired steps may beeliminated).

1-20. (canceled)
 21. A system comprising: a gateway of an integratedroofing mesh network node in communication with a mesh network of othernodes; wherein the gateway comprises a processor configured to executesoftware instructions that cause the processor to: control the gatewayto devote a predetermined percentage of bandwidth to a passthroughtraffic; determine the passthrough traffic based at least in part on: i)an address associated with the gateway, and ii) a source address and adestination address of each of a plurality of received data packets thatis received from the mesh network; wherein the passthrough trafficcomprises a subset of the plurality of received data packets, aplurality of transmitted data packets transmitted to the mesh network,or any combination thereof, that are routed between two or more of theother nodes of the mesh network through the gateway of the integratedroofing mesh network node based at least in part on the source addressand the destination address of each data packet; determine at least onemetric based at least in part on a passthrough data capacity based on apayload of data of each data packet in the subset; and communicate theat least one metric to a service provider to notify the service providerof an amount of mesh network bandwidth provided by the passthrough datacapacity of the integrated roofing mesh network node.
 22. The system ofclaim 21, wherein the processor is further configured to assess the atleast one metric relative to an amount of available bandwidth of thebandwidth to determine a portion of the passthrough data capacityattributed to the gateway of the integrated roofing mesh network node incomparison to other nodes.
 23. The system of claim 21, wherein the atleast one metric comprises a magnitude of the passthrough data capacity,a ratio of the magnitude and consumed data capacity, a ratio of thepassthrough data capacity to a total mesh network bandwidth, or acombination thereof.
 24. The system of claim 21, wherein the processoris further configured to determine the at least one metric over a periodof time defined by the service provider.
 25. The system of claim 21,wherein the processor is further configured to facilitate transmissionof at least one particular data packet of the plurality of transmitteddata packets using the gateway of the integrated roofing mesh networknode to an external destination address.
 26. The system of claim 21,wherein processor is further configured to transmit or receive each datapacket based on a header including the source address and thedestination address.
 27. The system of claim 21, wherein the processoris further configured to log a routing of each data packet, having thesource address, the destination address, or a combination thereof, of adevice local to the gateway, as a consumed traffic.
 28. The system ofclaim 21, wherein the processor is further configured to log each datapacket, having the source address, the destination address, or acombination thereof, of a device that is not local to the gateway, asthe passthrough traffic.
 29. The system of claim 21, wherein theprocessor is further configured to generate a visualization of the atleast one metric over a period of time.
 30. The system of claim 21,wherein the processor is further configured to generate an incentiverecommendation to instruct a rebate or reward for a contribution of theintegrated roofing mesh network node.
 31. The system of claim 30,wherein the incentive recommendation is based on the at least onemetric.
 32. The system of claim 31, wherein the incentive recommendationcomprises a reduction in a service bill, a rebate for additional dataservice, or a combination thereof.
 33. A roofing system comprising: aroof; and a gateway of an integrated roofing mesh network node of theroof in communication with a mesh network of other nodes; wherein thegateway comprises a processor configured to execute softwareinstructions that cause the processor to: receive a first set of datapackets from the mesh network; transmit a second set of data packets tothe mesh network; examine packet headers of the first set of datapackets and the second set of data packets; compare a source address anda destination address of each data packet in the first set of datapackets and each data packet in the second set of data packets to anaddress associated with the gateway; classify each data packet of thefirst set of data packets and the second set of data packets as aconsumed data packet if the source address or the destination addressmatches the address associated with the gateway; classify each datapacket of the first set of data packets and the second set of datapackets as a passthrough data packet if neither the source address northe destination address matches the address associated with the gateway;and determine an amount of mesh network bandwidth provided by apassthrough data capacity of the integrated roofing mesh network node,wherein the passthrough data capacity is based on at least one metricbased on a payload of each data packet of the first set of data packetsand the second set of data packets classified as a passthrough datapacket.
 34. The roofing system of claim 33, wherein the processor isfurther configured to monitor routing of the first set of data packets,the second set of data packets, or a combination thereof.
 35. Theroofing system of claim 33, wherein the processor is further configuredto determine a route for each data packet in the first set of datapackets, the second set of data packets, or a combination thereof. 36.The roofing system of claim 33, wherein the processor is furtherconfigured to allocate a broadcast address, a multicast address, or acombination thereof, for the mesh network.
 37. The roofing system ofclaim 33, wherein the processor is further configured to examine thepacket headers of the first set of data packets to determine the sourceaddress and the destination address.
 38. A method comprising: comparing,by utilizing a processor of a gateway of an integrated roofing meshnetwork node in communication with a mesh network of other nodes, asource address and a destination address of each data packet transmittedto and received by the integrated roofing mesh network node to anaddress associated with the gateway; classifying, by utilizing theprocessor of the gateway, each data packet as consumed traffic if thesource address or the destination address matches the address associatedwith the gateway and as passthrough traffic if neither the sourceaddress nor the destination address matches the address associated withthe gateway; examining, by utilizing the processor of the gateway, apacket payload for each data packet in the consumed traffic and eachdata packet in the passthrough traffic; determining, by utilizing theprocessor of the gateway, a payload size of each data packet based onthe packet payload of each data packet; measuring, based on the payloadsize and by utilizing the processor of the gateway, a consumed trafficthroughput, goodput, or any combination thereof, for each data packet inthe consumed traffic and a passthrough traffic throughput, goodput, orany combination thereof, for each data packet in the passthroughtraffic; and determining, by utilizing the processor of the gateway anamount of mesh network bandwidth provided by a passthrough data capacityof the integrated roofing mesh network node, wherein the passthroughdata capacity is based on at least one metric based on the passthroughtraffic throughput, goodput, or a combination thereof.
 39. The method ofclaim 38, further comprising determining, by utilizing the processor ofthe gateway, the payload size based on a memory footprint utilized tostore each data packet.
 40. The method of claim 38, further comprisingutilizing, by the processor of the gateway, a data communicationprediction model to predict the at least one metric.